{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 294,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 深圳市人才集团求职者信息及企业招聘岗位信息表分析"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 292,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>\n",
       "div.code_cell {\n",
       "    background-color: #fceae6\n",
       "}\n",
       "div.cell.selected {\n",
       "    background-color: #e6eefe;\n",
       "    font-size: 2rem;\n",
       "    line-height: 2.4rem;\n",
       "}\n",
       "div.cell.selected .rendered_html table {\n",
       "    font-size: 2rem !important;\n",
       "    line-height: 2.4rem !important;\n",
       "}\n",
       ".rendered_html pre code {\n",
       "    background-color: #C4E4ff;   \n",
       "    padding: 2px 25px;\n",
       "}\n",
       ".rendered_html pre {\n",
       "    background-color: #99c9ff;\n",
       "}\n",
       "div.code_cell .CodeMirror {\n",
       "    font-size: 2rem !important;\n",
       "    line-height: 2.4rem !important;\n",
       "}\n",
       ".rendered_html img, .rendered_html svg {\n",
       "    max-width: 60%;\n",
       "    height: auto;\n",
       "    float: right;\n",
       "}\n",
       "\n",
       ".rendered_html img[src*=\"#full\"], .rendered_html svg[src*=\"#full\"] {\n",
       "    max-width: 95%;\n",
       "    height: auto;\n",
       "}\n",
       "\n",
       ".rendered_html img[src*=\"#thumbnail\"], .rendered_html svg[src*=\"#thumbnail\"] {\n",
       "    max-width: 15%;\n",
       "    height: auto;\n",
       "}\n",
       "\n",
       "/* Gradient transparent - color - transparent */\n",
       "hr {\n",
       "    border: 0;\n",
       "    border-bottom: 1px dashed #ccc;\n",
       "}\n",
       ".emoticon{\n",
       "    font-size: 5rem;\n",
       "    line-height: 4.4rem;\n",
       "    text-align: center;\n",
       "    vertical-align: middle;\n",
       "}\n",
       ".bg-split_apply_comine {\n",
       "    width: 500px;     \n",
       "    height: 300px;\n",
       "    background: url('02_split-apply-comine_500x300.png') -10px -10px;\n",
       "    float: right;\n",
       "}\n",
       ".bg-comine {\n",
       "    width: 175px;\n",
       "    height: 150px;\n",
       "    background: url('02_split-apply-comine_500x300.png') -280px -80px;\n",
       "    float: right;\n",
       "}\n",
       ".bg-apply {\n",
       "    width: 155px;\n",
       "    height: 225px;\n",
       "    background: url('02_split-apply-comine_500x300.png') -160px -30px;\n",
       "    float: right;\n",
       "}\n",
       ".bg-split {\n",
       "    width: 205px;\n",
       "    height: 225px;\n",
       "    background: url('02_split-apply-comine_500x300.png') -10px -30px;\n",
       "    float: right;\n",
       "}\n",
       ".break {\n",
       "                   page-break-after: right; \n",
       "                   width:700px;\n",
       "                   clear:both;\n",
       "}\n",
       "</style>\n"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%%html\n",
    "<style>\n",
    "/* 本电子讲义使用之CSS */\n",
    "div.code_cell {\n",
    "    background-color: #fceae6\n",
    "}\n",
    "div.cell.selected {\n",
    "    background-color: #e6eefe;\n",
    "    font-size: 2rem;\n",
    "    line-height: 2.4rem;\n",
    "}\n",
    "div.cell.selected .rendered_html table {\n",
    "    font-size: 2rem !important;\n",
    "    line-height: 2.4rem !important;\n",
    "}\n",
    ".rendered_html pre code {\n",
    "    background-color: #C4E4ff;   \n",
    "    padding: 2px 25px;\n",
    "}\n",
    ".rendered_html pre {\n",
    "    background-color: #99c9ff;\n",
    "}\n",
    "div.code_cell .CodeMirror {\n",
    "    font-size: 2rem !important;\n",
    "    line-height: 2.4rem !important;\n",
    "}\n",
    ".rendered_html img, .rendered_html svg {\n",
    "    max-width: 60%;\n",
    "    height: auto;\n",
    "    float: right;\n",
    "}\n",
    "\n",
    ".rendered_html img[src*=\"#full\"], .rendered_html svg[src*=\"#full\"] {\n",
    "    max-width: 95%;\n",
    "    height: auto;\n",
    "}\n",
    "\n",
    ".rendered_html img[src*=\"#thumbnail\"], .rendered_html svg[src*=\"#thumbnail\"] {\n",
    "    max-width: 15%;\n",
    "    height: auto;\n",
    "}\n",
    "\n",
    "/* Gradient transparent - color - transparent */\n",
    "hr {\n",
    "    border: 0;\n",
    "    border-bottom: 1px dashed #ccc;\n",
    "}\n",
    ".emoticon{\n",
    "    font-size: 5rem;\n",
    "    line-height: 4.4rem;\n",
    "    text-align: center;\n",
    "    vertical-align: middle;\n",
    "}\n",
    ".bg-split_apply_comine {\n",
    "    width: 500px;     \n",
    "    height: 300px;\n",
    "    background: url('02_split-apply-comine_500x300.png') -10px -10px;\n",
    "    float: right;\n",
    "}\n",
    ".bg-comine {\n",
    "    width: 175px;\n",
    "    height: 150px;\n",
    "    background: url('02_split-apply-comine_500x300.png') -280px -80px;\n",
    "    float: right;\n",
    "}\n",
    ".bg-apply {\n",
    "    width: 155px;\n",
    "    height: 225px;\n",
    "    background: url('02_split-apply-comine_500x300.png') -160px -30px;\n",
    "    float: right;\n",
    "}\n",
    ".bg-split {\n",
    "    width: 205px;\n",
    "    height: 225px;\n",
    "    background: url('02_split-apply-comine_500x300.png') -10px -30px;\n",
    "    float: right;\n",
    "}\n",
    ".break {\n",
    "                   page-break-after: right; \n",
    "                   width:700px;\n",
    "                   clear:both;\n",
    "}\n",
    "</style>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "metadata": {},
   "outputs": [],
   "source": [
    "from flask import Flask,render_template\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import plotly as py\n",
    "import  plotly.graph_objects as go\n",
    "import seaborn as sns\n",
    "import requests\n",
    "from bs4 import BeautifulSoup\n",
    "import time\n",
    "from statsmodels.stats.anova import anova_lm\n",
    "from statsmodels.formula.api import ols"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "metadata": {},
   "outputs": [],
   "source": [
    "#分析应聘者数据\n",
    "def read_data():\n",
    "    return pd.read_csv('./datas/job/person.csv',\n",
    "                      sep=\",\",\n",
    "                      engine=\"python\",\n",
    "                      header=None,\n",
    "                      names=\"序号,性别,工龄,学历,专业,年龄,上一个岗位,上一个行业,目前位置,语言,特长\".split(\",\")\n",
    "                      )#重命名列名"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>序号</th>\n",
       "      <th>性别</th>\n",
       "      <th>工龄</th>\n",
       "      <th>学历</th>\n",
       "      <th>专业</th>\n",
       "      <th>年龄</th>\n",
       "      <th>上一个岗位</th>\n",
       "      <th>上一个行业</th>\n",
       "      <th>目前位置</th>\n",
       "      <th>语言</th>\n",
       "      <th>特长</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>PERSON_ID</td>\n",
       "      <td>GENDER</td>\n",
       "      <td>WORK_YEARS</td>\n",
       "      <td>HIGHEST_EDU</td>\n",
       "      <td>MAJOR</td>\n",
       "      <td>AGE</td>\n",
       "      <td>LAST_POSITION</td>\n",
       "      <td>LAST_INDUSTRY</td>\n",
       "      <td>CURR_LOC</td>\n",
       "      <td>LANGUAGE_REMARK</td>\n",
       "      <td>SPECILTY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>33291</td>\n",
       "      <td>男</td>\n",
       "      <td>15</td>\n",
       "      <td>大专</td>\n",
       "      <td>计算机应用技术</td>\n",
       "      <td>37</td>\n",
       "      <td>网络管理/信息安全管理</td>\n",
       "      <td>NaN</td>\n",
       "      <td>深圳市</td>\n",
       "      <td>普通话：精通  广东话：精通******语：听：良好，说：良好，读：良好，写：</td>\n",
       "      <td>1、精通计算机软、硬件及网络维护，能迅速处理各种突发的计算机故障 2、熟悉局域网建设和管理,...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2985277</td>\n",
       "      <td>男</td>\n",
       "      <td>12</td>\n",
       "      <td>大专</td>\n",
       "      <td>计算机应用技术</td>\n",
       "      <td>35</td>\n",
       "      <td>*公关/营销/业务类</td>\n",
       "      <td>文化体育行业</td>\n",
       "      <td>深圳市</td>\n",
       "      <td>普通话：精通  广东话：精通************语：听：精通，说：精通，读</td>\n",
       "      <td>..</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2982066</td>\n",
       "      <td>女</td>\n",
       "      <td>10</td>\n",
       "      <td>大专</td>\n",
       "      <td>金融学（含保险学）</td>\n",
       "      <td>32</td>\n",
       "      <td>出纳</td>\n",
       "      <td>医药销售行业</td>\n",
       "      <td>南山区</td>\n",
       "      <td>普通话：精通  广东话：精通************语：听：精通，说：良好，读</td>\n",
       "      <td>****-****年获校三等奖学金****-****年获单项奖学金</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3010866</td>\n",
       "      <td>男</td>\n",
       "      <td>10</td>\n",
       "      <td>中专</td>\n",
       "      <td>物理电子学</td>\n",
       "      <td>34</td>\n",
       "      <td>营销代表/销售顾问</td>\n",
       "      <td>珠宝玉石行业</td>\n",
       "      <td>深圳市</td>\n",
       "      <td>普通话：精通  广东话：良好************语：听：一般，说：不会，读</td>\n",
       "      <td>我对塑胶厂里的运作流程非常熟悉,从业务,计划,生产,人事,采购,客户服务都有经验.</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          序号      性别  ...                                       语言                                                 特长\n",
       "0  PERSON_ID  GENDER  ...                          LANGUAGE_REMARK                                           SPECILTY\n",
       "1      33291       男  ...  普通话：精通  广东话：精通******语：听：良好，说：良好，读：良好，写：  1、精通计算机软、硬件及网络维护，能迅速处理各种突发的计算机故障 2、熟悉局域网建设和管理,...\n",
       "2    2985277       男  ...  普通话：精通  广东话：精通************语：听：精通，说：精通，读                                                 ..\n",
       "3    2982066       女  ...  普通话：精通  广东话：精通************语：听：精通，说：良好，读                  ****-****年获校三等奖学金****-****年获单项奖学金\n",
       "4    3010866       男  ...  普通话：精通  广东话：良好************语：听：一般，说：不会，读          我对塑胶厂里的运作流程非常熟悉,从业务,计划,生产,人事,采购,客户服务都有经验.\n",
       "\n",
       "[5 rows x 11 columns]"
      ]
     },
     "execution_count": 141,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = read_data()\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 181,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>序号</th>\n",
       "      <th>性别</th>\n",
       "      <th>工龄</th>\n",
       "      <th>学历</th>\n",
       "      <th>专业</th>\n",
       "      <th>年龄</th>\n",
       "      <th>上一个岗位</th>\n",
       "      <th>上一个行业</th>\n",
       "      <th>目前位置</th>\n",
       "      <th>语言</th>\n",
       "      <th>特长</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>33291</td>\n",
       "      <td>男</td>\n",
       "      <td>15</td>\n",
       "      <td>大专</td>\n",
       "      <td>计算机应用技术</td>\n",
       "      <td>37</td>\n",
       "      <td>网络管理/信息安全管理</td>\n",
       "      <td>NaN</td>\n",
       "      <td>深圳市</td>\n",
       "      <td>普通话：精通  广东话：精通******语：听：良好，说：良好，读：良好，写：</td>\n",
       "      <td>1、精通计算机软、硬件及网络维护，能迅速处理各种突发的计算机故障 2、熟悉局域网建设和管理,...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>2985277</td>\n",
       "      <td>男</td>\n",
       "      <td>12</td>\n",
       "      <td>大专</td>\n",
       "      <td>计算机应用技术</td>\n",
       "      <td>35</td>\n",
       "      <td>*公关/营销/业务类</td>\n",
       "      <td>文化体育行业</td>\n",
       "      <td>深圳市</td>\n",
       "      <td>普通话：精通  广东话：精通************语：听：精通，说：精通，读</td>\n",
       "      <td>..</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>2982066</td>\n",
       "      <td>女</td>\n",
       "      <td>10</td>\n",
       "      <td>大专</td>\n",
       "      <td>金融学（含保险学）</td>\n",
       "      <td>32</td>\n",
       "      <td>出纳</td>\n",
       "      <td>医药销售行业</td>\n",
       "      <td>南山区</td>\n",
       "      <td>普通话：精通  广东话：精通************语：听：精通，说：良好，读</td>\n",
       "      <td>****-****年获校三等奖学金****-****年获单项奖学金</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>3010866</td>\n",
       "      <td>男</td>\n",
       "      <td>10</td>\n",
       "      <td>中专</td>\n",
       "      <td>物理电子学</td>\n",
       "      <td>34</td>\n",
       "      <td>营销代表/销售顾问</td>\n",
       "      <td>珠宝玉石行业</td>\n",
       "      <td>深圳市</td>\n",
       "      <td>普通话：精通  广东话：良好************语：听：一般，说：不会，读</td>\n",
       "      <td>我对塑胶厂里的运作流程非常熟悉,从业务,计划,生产,人事,采购,客户服务都有经验.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>316816964</td>\n",
       "      <td>女</td>\n",
       "      <td>15</td>\n",
       "      <td>中专</td>\n",
       "      <td>学前教育学</td>\n",
       "      <td>34</td>\n",
       "      <td>小学教育/幼儿教育/保育</td>\n",
       "      <td>行业组织</td>\n",
       "      <td>福田区</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1：拥有丰富的办公室工作经历，能独挡一面处理工作中的相关事情。2：熟练掌握Windows办公...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54771</th>\n",
       "      <td>54775</td>\n",
       "      <td>4546928</td>\n",
       "      <td>男</td>\n",
       "      <td>14</td>\n",
       "      <td>大专</td>\n",
       "      <td>NaN</td>\n",
       "      <td>47</td>\n",
       "      <td>产品开发</td>\n",
       "      <td>娱乐业</td>\n",
       "      <td>深圳市</td>\n",
       "      <td>NaN</td>\n",
       "      <td>★经验描述： 曾在所述行业或职位上成功开发过银行柜员机、自动售货机、医疗设备、双电源开关、塑...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54772</th>\n",
       "      <td>54776</td>\n",
       "      <td>6261517</td>\n",
       "      <td>男</td>\n",
       "      <td>26</td>\n",
       "      <td>大学本科</td>\n",
       "      <td>财务管理</td>\n",
       "      <td>48</td>\n",
       "      <td>财务总监CFO/总会计师</td>\n",
       "      <td>香料香精行业</td>\n",
       "      <td>广州市</td>\n",
       "      <td>文案及沟通能力较好，有较强的人际穿透能力。</td>\n",
       "      <td>一、在财务会计工作方面：1．能熟练处理工商企业全盘账税，熟练操作用友、金蝶、江苏速达等财务软...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54773</th>\n",
       "      <td>54777</td>\n",
       "      <td>5642172</td>\n",
       "      <td>男</td>\n",
       "      <td>13</td>\n",
       "      <td>大专</td>\n",
       "      <td>NaN</td>\n",
       "      <td>32</td>\n",
       "      <td>项目管理</td>\n",
       "      <td>银行业</td>\n",
       "      <td>深圳市</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.熟练使用PRO-E/CAD、UG、OFFICE软件；2.对模具结构及成型工艺有很深的理解...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54774</th>\n",
       "      <td>54778</td>\n",
       "      <td>5433634</td>\n",
       "      <td>男</td>\n",
       "      <td>13</td>\n",
       "      <td>博士研究生</td>\n",
       "      <td>管理科学与工程</td>\n",
       "      <td>39</td>\n",
       "      <td>总裁/总经理/CEO/主席/社长/总编</td>\n",
       "      <td>塑胶行业</td>\n",
       "      <td>深圳市</td>\n",
       "      <td>具有较强的语听说读写能力。可流利阅读文资料,并能熟练收发文邮件</td>\n",
       "      <td>****公司化企业财务总监工作经验；精通现代企业财务管理并具有深厚的财务管理理论与实践经验；...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54775</th>\n",
       "      <td>54779</td>\n",
       "      <td>4924201</td>\n",
       "      <td>男</td>\n",
       "      <td>26</td>\n",
       "      <td>硕士研究生</td>\n",
       "      <td>工商管理(MBA)</td>\n",
       "      <td>48</td>\n",
       "      <td>总裁/总经理/CEO/主席/社长/总编</td>\n",
       "      <td>塑胶行业</td>\n",
       "      <td>深圳市</td>\n",
       "      <td>语言水平：************         语:一般*********</td>\n",
       "      <td>主要项目和业绩：（只列举了其中的一小部分项目）1、成都***程建设项目（****年****月...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>54776 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       Unnamed: 0  ...                                                 特长\n",
       "0               1  ...  1、精通计算机软、硬件及网络维护，能迅速处理各种突发的计算机故障 2、熟悉局域网建设和管理,...\n",
       "1               2  ...                                                 ..\n",
       "2               3  ...                  ****-****年获校三等奖学金****-****年获单项奖学金\n",
       "3               4  ...          我对塑胶厂里的运作流程非常熟悉,从业务,计划,生产,人事,采购,客户服务都有经验.\n",
       "4               5  ...  1：拥有丰富的办公室工作经历，能独挡一面处理工作中的相关事情。2：熟练掌握Windows办公...\n",
       "...           ...  ...                                                ...\n",
       "54771       54775  ...  ★经验描述： 曾在所述行业或职位上成功开发过银行柜员机、自动售货机、医疗设备、双电源开关、塑...\n",
       "54772       54776  ...  一、在财务会计工作方面：1．能熟练处理工商企业全盘账税，熟练操作用友、金蝶、江苏速达等财务软...\n",
       "54773       54777  ...  1.熟练使用PRO-E/CAD、UG、OFFICE软件；2.对模具结构及成型工艺有很深的理解...\n",
       "54774       54778  ...  ****公司化企业财务总监工作经验；精通现代企业财务管理并具有深厚的财务管理理论与实践经验；...\n",
       "54775       54779  ...  主要项目和业绩：（只列举了其中的一小部分项目）1、成都***程建设项目（****年****月...\n",
       "\n",
       "[54776 rows x 12 columns]"
      ]
     },
     "execution_count": 181,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = df[df['学历'].str.contains('HIGHEST_EDU')==False ]#去除首行原列名\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.to_excel('./datas/job/person.xlsx')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 182,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>序号</th>\n",
       "      <th>性别</th>\n",
       "      <th>工龄</th>\n",
       "      <th>学历</th>\n",
       "      <th>专业</th>\n",
       "      <th>年龄</th>\n",
       "      <th>上一个岗位</th>\n",
       "      <th>上一个行业</th>\n",
       "      <th>目前位置</th>\n",
       "      <th>语言</th>\n",
       "      <th>特长</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>33291</td>\n",
       "      <td>男</td>\n",
       "      <td>15</td>\n",
       "      <td>大专</td>\n",
       "      <td>计算机应用技术</td>\n",
       "      <td>37</td>\n",
       "      <td>网络管理/信息安全管理</td>\n",
       "      <td>NaN</td>\n",
       "      <td>深圳市</td>\n",
       "      <td>普通话：精通  广东话：精通******语：听：良好，说：良好，读：良好，写：</td>\n",
       "      <td>1、精通计算机软、硬件及网络维护，能迅速处理各种突发的计算机故障 2、熟悉局域网建设和管理,...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>2985277</td>\n",
       "      <td>男</td>\n",
       "      <td>12</td>\n",
       "      <td>大专</td>\n",
       "      <td>计算机应用技术</td>\n",
       "      <td>35</td>\n",
       "      <td>*公关/营销/业务类</td>\n",
       "      <td>文化体育行业</td>\n",
       "      <td>深圳市</td>\n",
       "      <td>普通话：精通  广东话：精通************语：听：精通，说：精通，读</td>\n",
       "      <td>..</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>2982066</td>\n",
       "      <td>女</td>\n",
       "      <td>10</td>\n",
       "      <td>大专</td>\n",
       "      <td>金融学（含保险学）</td>\n",
       "      <td>32</td>\n",
       "      <td>出纳</td>\n",
       "      <td>医药销售行业</td>\n",
       "      <td>南山区</td>\n",
       "      <td>普通话：精通  广东话：精通************语：听：精通，说：良好，读</td>\n",
       "      <td>****-****年获校三等奖学金****-****年获单项奖学金</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>3010866</td>\n",
       "      <td>男</td>\n",
       "      <td>10</td>\n",
       "      <td>中专</td>\n",
       "      <td>物理电子学</td>\n",
       "      <td>34</td>\n",
       "      <td>营销代表/销售顾问</td>\n",
       "      <td>珠宝玉石行业</td>\n",
       "      <td>深圳市</td>\n",
       "      <td>普通话：精通  广东话：良好************语：听：一般，说：不会，读</td>\n",
       "      <td>我对塑胶厂里的运作流程非常熟悉,从业务,计划,生产,人事,采购,客户服务都有经验.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>316816964</td>\n",
       "      <td>女</td>\n",
       "      <td>15</td>\n",
       "      <td>中专</td>\n",
       "      <td>学前教育学</td>\n",
       "      <td>34</td>\n",
       "      <td>小学教育/幼儿教育/保育</td>\n",
       "      <td>行业组织</td>\n",
       "      <td>福田区</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1：拥有丰富的办公室工作经历，能独挡一面处理工作中的相关事情。2：熟练掌握Windows办公...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54771</th>\n",
       "      <td>54775</td>\n",
       "      <td>4546928</td>\n",
       "      <td>男</td>\n",
       "      <td>14</td>\n",
       "      <td>大专</td>\n",
       "      <td>NaN</td>\n",
       "      <td>47</td>\n",
       "      <td>产品开发</td>\n",
       "      <td>娱乐业</td>\n",
       "      <td>深圳市</td>\n",
       "      <td>NaN</td>\n",
       "      <td>★经验描述： 曾在所述行业或职位上成功开发过银行柜员机、自动售货机、医疗设备、双电源开关、塑...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54772</th>\n",
       "      <td>54776</td>\n",
       "      <td>6261517</td>\n",
       "      <td>男</td>\n",
       "      <td>26</td>\n",
       "      <td>大学本科</td>\n",
       "      <td>财务管理</td>\n",
       "      <td>48</td>\n",
       "      <td>财务总监CFO/总会计师</td>\n",
       "      <td>香料香精行业</td>\n",
       "      <td>广州市</td>\n",
       "      <td>文案及沟通能力较好，有较强的人际穿透能力。</td>\n",
       "      <td>一、在财务会计工作方面：1．能熟练处理工商企业全盘账税，熟练操作用友、金蝶、江苏速达等财务软...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54773</th>\n",
       "      <td>54777</td>\n",
       "      <td>5642172</td>\n",
       "      <td>男</td>\n",
       "      <td>13</td>\n",
       "      <td>大专</td>\n",
       "      <td>NaN</td>\n",
       "      <td>32</td>\n",
       "      <td>项目管理</td>\n",
       "      <td>银行业</td>\n",
       "      <td>深圳市</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.熟练使用PRO-E/CAD、UG、OFFICE软件；2.对模具结构及成型工艺有很深的理解...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54774</th>\n",
       "      <td>54778</td>\n",
       "      <td>5433634</td>\n",
       "      <td>男</td>\n",
       "      <td>13</td>\n",
       "      <td>博士研究生</td>\n",
       "      <td>管理科学与工程</td>\n",
       "      <td>39</td>\n",
       "      <td>总裁/总经理/CEO/主席/社长/总编</td>\n",
       "      <td>塑胶行业</td>\n",
       "      <td>深圳市</td>\n",
       "      <td>具有较强的语听说读写能力。可流利阅读文资料,并能熟练收发文邮件</td>\n",
       "      <td>****公司化企业财务总监工作经验；精通现代企业财务管理并具有深厚的财务管理理论与实践经验；...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54775</th>\n",
       "      <td>54779</td>\n",
       "      <td>4924201</td>\n",
       "      <td>男</td>\n",
       "      <td>26</td>\n",
       "      <td>硕士研究生</td>\n",
       "      <td>工商管理(MBA)</td>\n",
       "      <td>48</td>\n",
       "      <td>总裁/总经理/CEO/主席/社长/总编</td>\n",
       "      <td>塑胶行业</td>\n",
       "      <td>深圳市</td>\n",
       "      <td>语言水平：************         语:一般*********</td>\n",
       "      <td>主要项目和业绩：（只列举了其中的一小部分项目）1、成都***程建设项目（****年****月...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>54776 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       Unnamed: 0  ...                                                 特长\n",
       "0               1  ...  1、精通计算机软、硬件及网络维护，能迅速处理各种突发的计算机故障 2、熟悉局域网建设和管理,...\n",
       "1               2  ...                                                 ..\n",
       "2               3  ...                  ****-****年获校三等奖学金****-****年获单项奖学金\n",
       "3               4  ...          我对塑胶厂里的运作流程非常熟悉,从业务,计划,生产,人事,采购,客户服务都有经验.\n",
       "4               5  ...  1：拥有丰富的办公室工作经历，能独挡一面处理工作中的相关事情。2：熟练掌握Windows办公...\n",
       "...           ...  ...                                                ...\n",
       "54771       54775  ...  ★经验描述： 曾在所述行业或职位上成功开发过银行柜员机、自动售货机、医疗设备、双电源开关、塑...\n",
       "54772       54776  ...  一、在财务会计工作方面：1．能熟练处理工商企业全盘账税，熟练操作用友、金蝶、江苏速达等财务软...\n",
       "54773       54777  ...  1.熟练使用PRO-E/CAD、UG、OFFICE软件；2.对模具结构及成型工艺有很深的理解...\n",
       "54774       54778  ...  ****公司化企业财务总监工作经验；精通现代企业财务管理并具有深厚的财务管理理论与实践经验；...\n",
       "54775       54779  ...  主要项目和业绩：（只列举了其中的一小部分项目）1、成都***程建设项目（****年****月...\n",
       "\n",
       "[54776 rows x 12 columns]"
      ]
     },
     "execution_count": 182,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df=pd.read_excel('./datas/job/person.xlsx')\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 211,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0           大专\n",
       "1           大专\n",
       "2           大专\n",
       "3           中专\n",
       "4           中专\n",
       "         ...  \n",
       "54771       大专\n",
       "54772     大学本科\n",
       "54773       大专\n",
       "54774    博士研究生\n",
       "54775    硕士研究生\n",
       "Name: 学历, Length: 54776, dtype: object"
      ]
     },
     "execution_count": 211,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"学历\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 212,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['大专', '中专', '大学本科', '高中（职高、技校）', '硕士研究生', '博士后', '其它', '博士研究生'],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 212,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.unique(df[\"学历\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 213,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "大专           25289\n",
       "大学本科         17392\n",
       "高中（职高、技校）     5786\n",
       "中专            5642\n",
       "硕士研究生          430\n",
       "其它             212\n",
       "博士后             14\n",
       "博士研究生           11\n",
       "Name: 学历, dtype: int64"
      ]
     },
     "execution_count": 213,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "counts=df['学历'].value_counts()\n",
    "counts#统计各学历总数"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 265,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 265,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAX0AAAFICAYAAAC8zi5PAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjMuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8vihELAAAACXBIWXMAAAsTAAALEwEAmpwYAAAdLElEQVR4nO3df5heZX3n8fcnExIxAQwSgmhjxEbFChGaIliUIL/kh1vEH3hBdSvdjUW2Xm21XRCkYt0W0VqLXVhpKbIoCApyUUANKkgUupjsCljXimJA2WqDYGIUISaf/eM+Q54ZJskzk8mc8+T+vK5rrjnnfubHd+bMfJ7z3Oc+9y3bREREHaa1XUBEREydhH5EREUS+hERFUnoR0RUJKEfEVGR6W0XsCV77LGHFyxY0HYZEREDZeXKlQ/bnjvWY50O/QULFrBixYq2y4iIGCiSHtjcY+neiYioSEI/IqIiCf2IiIok9CMiKpLQj4ioSEI/IqIiCf2IiIok9CMiKpLQj4ioyFbvyJW0G/ApYAj4OXAy8F3g/uZD/tD2vZIuBV4M3GT7/c3n9tU2GRacedNkfaknrTr/+En/mhERbernTP9U4MO2jwZ+BJwJXGV7SfN2r6STgCHbhwD7SFrYb9t2+rkiImIMWw192xfZvqXZnQv8CjhB0l2SLpU0HVgCXNN8zDLg0HG0jSBpqaQVklasXr16Ij9TRERsRt99+pIOAeYAtwBH2j4I2Ak4DpgFPNR86CPAvHG0jWD7EtuLbS+eO3fMSeIiImKC+pplU9LuwEeB1wE/sv1489AKYCGwDti5aZtNeTLpty0iIqbIVkNX0gzg08BZth8ArpC0SNIQcCJwN7CSTV01i4BV42iLiIgp0s+Z/u8DBwJnSzobuBW4AhBwg+0vStoVWC5pb+BY4GDAfbZFRMQU2Wro274YuHhU83mjPmatpCXAUcAFttcA9NsWERFTY9JWzrL9KJtG5oyrLSIipkYupEZEVCShHxFRkYR+RERFEvoRERVJ6EdEVCShHxFRkYR+RERFEvoRERVJ6EdEVCShHxFRkYR+RERFEvoRERVJ6EdEVCShHxFRkYR+RERFEvoRERVJ6EdEVCShHxFRkYR+RERFEvoRERVJ6EdEVCShHxFRkYR+RERFEvoRERVJ6EdEVCShHxFRkYR+RERFEvoRERVJ6EdEVCShHxFRkYR+RERFEvoRERXZauhL2k3S5yQtk/RZSTMkXSrpTknn9HzchNsiImJq9HOmfyrwYdtHAz8C3gQM2T4E2EfSQkknTbRt+/xYERExlulb+wDbF/XszgV+F/hIs78MOBQ4ALhmgm339X4/SUuBpQDz588fz88SERFb0XefvqRDgDnAD4CHmuZHgHnArG1oG8H2JbYX2148d+7ccf0wERGxZX2FvqTdgY8CpwHrgJ2bh2Y3X2Nb2iIiYor0cyF3BvBp4CzbDwArKd0yAIuAVdvYFhERU2SrffrA7wMHAmdLOhu4DHizpL2BY4GDAQPLJ9gWERFTZKtn+rYvtj3H9pLm7XJgCfDPwOG219heO9G27fFDRUTE2Po5038K24+yaRTONrdFRMTUyIXUiIiKJPQjIiqS0I+IqEhCPyKiIgn9iIiKJPQjIiqS0I+IqEhCPyKiIgn9iIiKJPQjIiqS0I+IqEhCPyKiIgn9iIiKJPQjIioyoamVY+IWnHnTpH69VecfP6lfLyJ2bDnTj4ioSEI/IqIiCf2IiIok9CMiKpLQj4ioSEI/IqIiCf2IiIok9CMiKpLQj4ioSEI/IqIiCf2IiIok9CMiKpLQj4ioSEI/IqIiCf2IiIok9CMiKpLQj4ioSF+hL2mepOXN9nRJD0q6rXnbr2m/VNKdks7p+by+2iIiYmpsNfQlzQEuB2Y1TfsDV9le0rzdK+kkYMj2IcA+khb227Z9fqyIiBhLP2f6G4CTgbXN/sHACZLuas7apwNLgGuax5cBh46jbQRJSyWtkLRi9erV4/15IiJiC7Ya+rbX2l7T0/R14EjbBwE7AcdRXgU81Dz+CDBvHG2jv98lthfbXjx37tzx/0QREbFZ0yfwOffYfrzZXgEsBNYBOzdtsylPJv22RUTEFJlI6F4haZGkIeBE4G5gJZu6ahYBq8bRFhERU2QiZ/rvA64EBNxg+4uSdgWWS9obOJbS7+8+2yIiYor0Hfq2lzTvv0kZwdP72FpJS4CjgAuGrwH02xYREVNjImf6Y7L9KJtG5oyrLSIipkYupEZEVCShHxFRkYR+RERFEvoRERVJ6EdEVCShHxFRkYR+RERFEvoRERVJ6EdEVCShHxFRkYR+RERFEvoRERVJ6EdEVCShHxFRkYR+RERFEvoRERVJ6EdEVCShHxFRkYR+RERFEvoRERVJ6EdEVCShHxFRkYR+RERFEvoRERVJ6EdEVCShHxFRkYR+RERFEvoRERVJ6EdEVCShHxFRkYR+RERFEvoRERXpK/QlzZO0vGf/Ukl3SjpnMtoiImJqbDX0Jc0BLgdmNfsnAUO2DwH2kbRwW9q21w8WERFP1c+Z/gbgZGBts78EuKbZXgYcuo1tI0haKmmFpBWrV6/u+weJiIit22ro215re01P0yzgoWb7EWDeNraN/n6X2F5se/HcuXPH99NERMQWTeRC7jpg52Z7dvM1tqUtIiKmyERCdyWbumUWAau2sS0iIqbI9Al8zvXAckl7A8cCBwPehraIiJgifZ/p217SvF9LuSD7z8DhttdsS9uk/SQREbFVEznTx/ajbBqFs81tERExNXIhNSKiIgn9iIiKJPQjIiqS0I+IqEhCPyKiIgn9iIiKJPQjIiqS0I+IqEhCPyKiIgn9iIiKJPQjIiqS0I+IqEhCPyKiIgn9iIiKJPQjIiqS0I+IqEhCPyKiIgn9iIiKJPQjIiqS0I+IqEhCPyKiIgn9iIiKJPQjIiqS0I+IqEhCPyKiIgn9iIiKJPQjIioyve0ConsWnHnTpH/NVecfP+lfMyLGL2f6EREVyZl+DKy8IokYv5zpR0RUJKEfEVGRcYe+pOmSHpR0W/O2n6RLJd0p6Zyej+urLSIips5EzvT3B66yvcT2EmAhMGT7EGAfSQslndRP22T9EBER0Z+JXMg9GDhB0uHAvcDjwDXNY8uAQ4ED+my7b/QXl7QUWAowf/78CZQXERGbM5Ez/a8DR9o+CNgJOBZ4qHnsEWAeMKvPtqewfYntxbYXz507dwLlRUTE5kwk9O+x/W/N9gpgD2DnZn928zXX9dkWERFTaCLBe4WkRZKGgBOBMyhdNQCLgFXAyj7bIiJiCk2kT/99wJWAgBuA64HlkvamdPUcDLjPtoiImELjDn3b36SM4HmSpCXAUcAFtteMpy0iIqbOpEzDYPtRNo3MGVdbRERMnVxMjYioSEI/IqIiCf2IiIok9CMiKpLQj4ioSEI/IqIiCf2IiIok9CMiKpLQj4ioSEI/IqIiCf2IiIok9CMiKpLQj4ioSEI/IqIiCf2IiIok9CMiKpLQj4ioSEI/IqIiCf2IiIok9CMiKpLQj4ioSEI/IqIiCf2IiIok9CMiKpLQj4ioSEI/IqIiCf2IiIok9CMiKpLQj4ioSEI/IqIi09suIGJHt+DMmyb9a646//hJ/5pRh5zpR0RUpJUzfUmXAi8GbrL9/jZqiIiR8oqkDlN+pi/pJGDI9iHAPpIWTnUNERG1ku2p/YbShcDnbd8s6U3AzrYv63l8KbC02X0h8K+TXMIewMOT/DW3h9Q5uVLn5BmEGqHuOp9re+5YD7TRvTMLeKjZfgQ4sPdB25cAl2yvby5phe3F2+vrT5bUOblS5+QZhBohdW5OGxdy1wE7N9uzW6ohIqJKbQTuSuDQZnsRsKqFGiIiqtRG9871wHJJewPHAgdP8fffbl1Hkyx1Tq7UOXkGoUZInWOa8gu5AJLmAEcBt9v+0ZQXEBFRqVZCPyIi2pGLqBERFUnoR0RUJKEfEVGRakJf0lFt1zAekt7Wdg3RLkkL2q5hmCS1XUM/JO0paV7bdUzUVBzzHT70Jb1R0jTgz5v957Zc0lNIOlzSK3veDgOOk3R8sz9QT1hdMgi/u2bqkdFtQ8DlLZSzObdL+nLz9zgbQNLLJd0p6fC2i5P0Ckm7U+79WSTpP0l6r6RzJf25pPPbrrFXm8d8hw994AzAwBPN/rebUEXSb7RW1UjHAq8GXttsH0Op+bRm/+j2ShtMg/Bk3+PNUP7pJd0MYHsDm/5mO8H2q4DfAP642b8D+C+U/7G2vYUyV9dGYANwCrAM+BJwHHBDe6WNqbVjXkPob/DIcan3Ae+RdAZwUUs1jWD7z4C7gJcCZ1MmYLoBuNP2Wbb/tMXyBtUgPNkP+xU8+U+/c097l8ZTD9fyScpJybBvArtPfTlP8Q1gH3p+Z7bvsP01YG3zBNUlrR3zHXblrKYPcglPfWJ7mPLMfyvwd1Nc1pgkzQBeBZwAnA78GLgG+Ps26xpwG2y7pyt6+Mn+JcAbgcNaq6wh6TTKP/88SW8BNGq7EyS9HtgJwPZaSd+Q9Dzb32+5tF73UyZv/LcxHuvMk2cXjvkOG/rADOA/APtJuhZ4tqQ9AWw/IWkWcG2bBfZYbPsdTXeEgKuBM9k0G2n0aZCe7ClnyOspgfoMyrHv3W6dpBdRuh2f3tP8GUq340V0pE5gNfCctovoQ+vHfIcNfduPA38s6aXA2yn/7NcB0yU9D7jWdlf6TN8t6b9SZiA9BdiLMkfRv0uaD+xk+3st1vek5iLenwDPp/SdAlxt+wvtVTXCwDzZ2/4QgKQTbV/YbP9Oz3bry07Z/jZwqqTbJe1BORkZAg6X9Bxgb2BtmzU2fgL0zh///yR9kXKW/0xJXwFOsP2zVqprdOGY77Ch38v2jyX9mNKFsovttcBftFxWr5soZ03fAV4EvAQ4mfJENXwm8HttFTdM0q8D5wPn2b63aXsa8E5JR9l+V6sFMlhP9pJOBA6iTDFO80rvaW3WtBU/Bz5H6Z64vmkzcE9bBfX4CeUi8xso13H+CfgecLft9W0W1qsLx7yG0P+H5hc7o7mg24WzktHuAp5LuYi7L6Ub4g+B3wLeYfvBFmvrdRpwWvOkCYDtXwL/TdJpkva33YUAAAbiyf5rlH/4l0i6iTLS6FB4MgxmtFjbaKIE/hW2L227mNGaaw3nUrr1ZgLzKP8/fyNpHfBu2/+nzRobrR/zKiZca8a/njV6EXZJv2b7By2V1VvHTpQ+058Bh9m+tWk/AFht+4dt1jeIJJ0KXAUst/3bbdezNZIOBGbbvr2n7Tdtr2yxrM1qrp0cZ3vyV1OfZJKWAA/avr/lUkZo65jXMGQTyjPra3obmq6K5ZJ2HvtTpo7t9bbXAHsMB35jY9cCvxnJgaTnSpo76rEj26nqqWx/0vZG4K/armVLJM2XNM/2/x71z380sEuLpY0g6X+O0fz25rF/HP230CZJr+3dt31blwK/7WNeS+j/gp6bHpqg/zil6+Sxtorq1bwaubpn/5nAdZIWtlfVmE5oLuZeCxwxfJG0uUDatbsebwaWSrpB0o3NcM2uWQG8S9J/lPRHPe3r6ch9JJJ+D9hf0luG3yg3F21s7iz9ue3VrRY50nt6dyRNl/QnbRUzhlaPeQ19+jTjtQ0g6QWUlWoutN2Zu/Rsb5D0KwBJuwGfBi63fV+7lW3SPFmuB36N0hf5U+DmZpTMrsCF7VU3pl2AUyknN1dQLpR3zd3AecAdwKskvYIyKupXzVsXPIOSFQdQXjUfAfx3Sj//0ZQRZ13y+Kj9jZR7Mz7cQi1jafWY79ChL+lYYDdKX/k8SZdSxskutd3FABiSdDLlBq2/Bv4UeF+7JRXNmfwyys0v3wG+1wyB/G1gOTDL9llt1jiGx4Yvgkt6rCujdsZwOvA3th+W9ClgePjrxS3W9CTbH5F0BOVEZBfghbb/VtKRtl/fcnljGXGh0vbG4ZO+DmntmO/QoU8J+PmUccWzKCNkfk63RkUM36W3gXJT0UzgSkrtu0l6K2Xk0cfaqxBs/7w5I/kY5cacP2hGS1xIOetbKOnNtq9os0548vf5BLCXpFMoZ6S92zNsX9ZmjQDNE6aBS5prOgDfsn1ai2UNJElvp5wlC9hTIyc0Ex25K7cLx3yHDn3bnxzelnSM7SMlvRj4O0mXdSGgGk+ndJtAeSlqysvoaZR5OYZaqmuE5oxplu0bJe1KCf/Dmq6pmZQz/i78Tp9NeYk/k3Kjm3q2h2jGSLdJZf6fMwBsr5F0COVV3b4q80L9Y4euNy2l/E6Ppfw97tX060vSX1GGQ7YdqnOAXzbb0+nQRfBhXTnmVQzZBJB0u+1XNtszgc8Df2n7lnYr20TScsowwzcC51Dqe2W7VY0kaS/KHblnApcBX7B9ZfPYqcA1XbkZRtItto8avd0lkm4BPksZbHA7Zc6l6ymjzY63/XB71RWS3gm8lafOBXUEpeZdbZ875YVthqQ7bR8yqu0O2y9vq6ZebR/zHfpMf1hzAXKn4X3bj0s6HficpP1sr2uvuhEes32RpKspZ8y3tVzPCJI+Q3lFsoQyz8l+wE6STqC8KpnR++qqA7yZ7S4xpTtvGWVm1Udtv1/S/ZQ5gt7UZnEAtv9a0uG2/7a3venT/5Ck6yTN79BNhCOOdXPTUydeLTdaPeZVnOk3B33B6LG6TR/v1S7Tm7aqGbL5JdtLmv2ZwBeBj9juxFwxkp5F6X76AmV46auBVZQ+/fuBmbY7M0mcpG9R+vZNOcF5q+0V7VY1kqT7KNdJfkCZWnuG7Xf2PHZMl8aYD2v+p95i++OSZjbTX3SCpJW2f7NnfyfK1CHvbrGsJ7V9zKsI/UEhaY7tR3v2n0e5QeuBFssaQZKAk21/qtl/PTBk++otf2aMRdLrgPW2b5D0tGZai+HHXkM5EfhFexUOhuYkaZ7tByW9wfanm/YPumPrUbR9zBP6HdJcZH7C9nebcF1s++tt19VL0hLbt43RPhd4VcK/f5JeaPtfm+1pwNtsd2KY5qBpRpYdDCxk0yier1FW+frPlOlMujDlSuvHvJY7cjtNm5byW0ZZ9g3KH+0H26loi86Gclu+pA81r0agzAr6jNaqGkyfULMuMvBK4HSNXCv5iLYLHATNCdIrKF2PL27e70/p2tudcoH0c82Is7a1fsyruJDbZU3g3yHpL4Dv2D5X0sWUeU069TKs6RsdrmkO5aLT+ZK+TVmJqnOjYzpuV2AxmxbPmD1qfwZljdfYguaO+/WU393uwALK3+dPge/bPk/SnXTj/6n1Y57Qb5ntB1Smhngnm0YYLKWsTtSFP9JeX6aMKb4FeJHtuyS9F7gZ+ERXhmoOkIcpQ4eh/NO/iTL1xk/aK2lgrWfTPS697yXpGOC7bnkBlUbrxzyh3w37UBZSWdLs/y/K3BwLJF0E/MT2ezbzuVPG9iuGx7tL+ryk64FHKC9Trxp9ITq2am/gd9l0lrc38BGVJQqvAy7owsiyAfEZyrDsl1K6rUXp3plOeQXalSmqWz/mCf2WqcxY+feURVOGrQPeQRmr/zHgA5Je5LJ0XdtWNReg/oUyDG4tgKRLKBfMLmizuEHR9EOfOXzhu/md/rLpitgJ+BBlioszWixzkDwBPEiZTPFplCeBmZThkK2v6AbdOea5kNsy2+tsH0yZeW/YUBPw62zfDXwU+FErBT7V+ylr0H6LcrFs2OfpztlU5zXTFpze03QzsFjSP1EmtHuAcld29Od5wH3Ao5QBBfdQllB8l6QDJR3UYm1Ad455zvQ7QGVukwOb7WnAnc1DBnBHVieS9CXKy9JnUv6hnifp3qZtOtCJm18GyL4qc9d8lfI7fR3ld/lpypnqmi18boz06ub93pR5gh5jUxcKlBPcu6a6qDG0fswzTr8DmjlrrgW+S1mD9Kwm/L9i+xXtVjeSyqIuR9q+uPnjvcX2l1VWzVpiO2enfWhe6t9GGUd+GPA2ysiTT1FCYAg4O9dIxqcZQvzcse4laVtXjnlCv0Mk7UeZymBF8wdyoO2V6shavgCSdgH2tP29ZrjpM2zfLWmay/KE0SeVeZ/ubbanUe6B+B/u1ipUMYm6cMwT+h2islDJl22/rKft1ylz8Ozblal2Y/JJeq3tz7ZdR0ydto55LuR2S+fX8o3tpuvrusbka+WYJ/Q7pLm637uW7+fo2Fq+sd1sbl3X2HG1cswzeqcDNHhr+cbkG4R1XWNytXLME/rdMBBr+cbk0oCs6xqTpwvHPKHfAR6ctXxjcnV+XdeYdK0f84ze6RgNwFq+Y5H0ZuDGjCufGHV8XdeYfG0d81zI7RCNsZYv5bbtS5o5ejpH0gskfRV4A+UOw5iYrq/rGpOvlWOe7p1ueRw4tbfB9rclnU25rbyLZlNW//mXtgsZcDNH7Q+RufR3dK0c83TvRLSgueP6yOFuO41c11XA8ynrqHZmfeTYdurAWr4J/eibpGcAuw0HUbP/5L0FwM+cP6i+SfoCpVvsHuCOpnka5ULfIcBptr/WUnmxHagDa/mmeyfGY1/KIhXDCzlfR1kJSM3bfKD1KWwHyJ7Ay4HlwIeBRcC3Kb/H6xL4O5aetXwfp6zl+w3gAOBWNq3l+wZJLx9ep2J7SOjHeGwENkq6nDL/N7afvINQ0imSlLP9vk0HjqC8UppGuYh/PGUGxitbrCu2g66s5ZvQj4nYh7JgBZJeA5xF6X8+rNWqBkhz1vcDyqI0d1NWexqe//0rwGGS1ti+qqUSY/tofS3fhH70pRlOegzwY8of7vAi6KIsnnJuS6UNqmnASttrJD0fmEeZbG94QZpdKF0BsWNpfS3fhH70ayblD/ULjFyRCMqZSoyD7Q2SFvdsfxW4kfK7PRF4r+1LWywxto/W1/JN6EdfbP9U0gcowd87YmcjpXsnxu+3JC0DbqFMtjcc+q+krEEcO57htXxnUgZGDN9p/y5JBwLTbW/XZR0T+jERc4AXANi+kRJWMQ7N3ZcrKYvMHwdsAM6jjIz6MGWt19jxtL6Wb8bpR98kHQwcYPviZv9WYMRcO7ZPaqO2QSRpT9v/3mwPAacBH7e9fsufGYOuzbV8E/rRN0l7As+0/X+b/V0pZyamWdjZ9k9aLDEitiKhH5Oi6Y98ZtdnA42oXfr0o2+SvgP8cHiX0ie5GLiAclHqz1oqLSL6lNCP8XjI9quGd5o+fYBbbf9BSzVFxDhkPv0Yj9F9gabMI5ILjxEDIn360TdJP2bTcDIBLwNeCFzY7J++PSeKiohtl+6d6Iuk6cDHbJ87qn02cCfwXeAc0q8f0Wk504++SXoEWEG5q/D7zftVwP7AvcAutl/WWoERsVXp04/x+Ibto4Ef9rw/CrgJeHMCP6L7cqYffZP0MGWFp6cDv+h5P5/S9XPxFj49IjogffrRl2aagD+y/YkxHnsB8Kypryoixitn+hERFUmffvRN0gebxdDHeuztkvaf4pIiYpwS+jEeHwX+oZltEwBJu0j6S+BZtu9pr7SI6Ee6d2JcmmUTz6AM01wP/Ar4pO3bWy0sIvqS0I+IqEi6dyIiKpLQj4ioSEI/IqIiCf2IiIok9CMiKpLQj4ioSObeiXGTdDNlfD6UE4czbX+zxZIiok8J/ZiIXYBTKYF/BfCddsuJiH4l9GMiHrP9IICkx2w/0XZBEdGfhH70TdJpwBPAXpJOoayL27s9w/ZlbdYYEVuW0I/xeDbwODAT2IsS9MPbQ8Ds9kqLiH5k7p0YN0m3NMskjtiOiO7LkM2YCG9mOyI6Lt07MRHPkfQNSuBPl7TY9oqWa4qIPqR7J/rWrJq1m+0Hmv05lODf2HzIz5w/qIhOy5l+jMe+wEuBi5v9a4GHKRd0BcwHDmqlsojoS0I/xmMjsFHS5TQ3ZNl+4/CDkk6RpJztR3RXLuTGROxDObNH0msk3SHpK7avTOBHdFtCP/rSrI17TLO7vnmDEv7vBja0UVdEjE9CP/o1k9KfD81Zfo+NRMRASOhHX2z/FPjA8C6bxudvBM5qo6aIGL9cyI2JmAO8AMD2jcCN7ZYTEf1K6Md4CJhm+wAASbdKuq73A2yf1EplEdGXhH6Mx/3Amp7936F0EZryhDDURlER0b/ckRsRUZFcyI2IqEhCPyKiIgn9iIiKJPQjIiry/wFKJrHlYGK9ywAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['学历'].value_counts().plot(kind='bar')#该批应聘者分学历统计数量"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 266,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:ylabel='性别'>"
      ]
     },
     "execution_count": 266,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['性别'].value_counts().plot(kind='pie')#该批应聘者性别比例"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 214,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "df['学历'].value_counts().to_excel('./datas/job/学历统计.xlsx')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 215,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "大专           25289\n",
       "大学本科         17392\n",
       "高中（职高、技校）     5786\n",
       "中专            5642\n",
       "硕士研究生          430\n",
       "其它             212\n",
       "博士后             14\n",
       "博士研究生           11\n",
       "Name: 学历, dtype: object"
      ]
     },
     "execution_count": 215,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "counts = counts.astype('str')\n",
    "counts#转换str格式"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 216,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.rcParams['font.sans-serif'] = ['SimHei']\n",
    "plt.rcParams['axes.unicode_minus'] = False\n",
    "edu = [14, 11, 430, 17392, 25289, 5786, 5642, 212]\n",
    "\n",
    "plt.barh(range(8), edu, height=0.7, color='steelblue', alpha=0.8)      # 从下往上画\n",
    "plt.yticks(range(8), ['博士后','博士研究生','硕士研究生','大学本科','大专','高中','中专','其他'])\n",
    "plt.xlim(0,27000)\n",
    "plt.xlabel(\"人数\")\n",
    "plt.title(\"应聘者学历统计\")\n",
    "for x, y in enumerate(edu):\n",
    "    plt.text(y + 0.2, x - 0.1, '%s' % y)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 217,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>性别</th>\n",
       "      <th>学历</th>\n",
       "      <th>年龄</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>女</td>\n",
       "      <td>中专</td>\n",
       "      <td>29.492918</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>女</td>\n",
       "      <td>其它</td>\n",
       "      <td>30.092593</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>女</td>\n",
       "      <td>博士后</td>\n",
       "      <td>23.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>女</td>\n",
       "      <td>博士研究生</td>\n",
       "      <td>36.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>女</td>\n",
       "      <td>大专</td>\n",
       "      <td>31.596881</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>女</td>\n",
       "      <td>大学本科</td>\n",
       "      <td>32.188040</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>女</td>\n",
       "      <td>硕士研究生</td>\n",
       "      <td>33.302013</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>女</td>\n",
       "      <td>高中（职高、技校）</td>\n",
       "      <td>29.599684</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>男</td>\n",
       "      <td>中专</td>\n",
       "      <td>33.116024</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>男</td>\n",
       "      <td>其它</td>\n",
       "      <td>45.822785</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>男</td>\n",
       "      <td>博士后</td>\n",
       "      <td>135.300000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>男</td>\n",
       "      <td>博士研究生</td>\n",
       "      <td>36.700000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>男</td>\n",
       "      <td>大专</td>\n",
       "      <td>33.815596</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>男</td>\n",
       "      <td>大学本科</td>\n",
       "      <td>33.409266</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>男</td>\n",
       "      <td>硕士研究生</td>\n",
       "      <td>43.096085</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>男</td>\n",
       "      <td>高中（职高、技校）</td>\n",
       "      <td>33.149401</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   性别         学历          年龄\n",
       "0   女         中专   29.492918\n",
       "1   女         其它   30.092593\n",
       "2   女        博士后   23.500000\n",
       "3   女      博士研究生   36.000000\n",
       "4   女         大专   31.596881\n",
       "5   女       大学本科   32.188040\n",
       "6   女      硕士研究生   33.302013\n",
       "7   女  高中（职高、技校）   29.599684\n",
       "8   男         中专   33.116024\n",
       "9   男         其它   45.822785\n",
       "10  男        博士后  135.300000\n",
       "11  男      博士研究生   36.700000\n",
       "12  男         大专   33.815596\n",
       "13  男       大学本科   33.409266\n",
       "14  男      硕士研究生   43.096085\n",
       "15  男  高中（职高、技校）   33.149401"
      ]
     },
     "execution_count": 217,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "应聘者分学历平均年龄 = df.groupby(by = [\"性别\",\"学历\"])\\\n",
    "                         .agg({\"年龄\":\"mean\"})\\\n",
    "                         .reset_index()\n",
    "应聘者分学历平均年龄#.数量.to_dict() #.index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 230,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>性别</th>\n",
       "      <th>学历</th>\n",
       "      <th>工龄</th>\n",
       "      <th>专业</th>\n",
       "      <th>目前位置</th>\n",
       "      <th>年龄</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>女</td>\n",
       "      <td>中专</td>\n",
       "      <td>0</td>\n",
       "      <td>【中医学】</td>\n",
       "      <td>深圳市</td>\n",
       "      <td>30.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>女</td>\n",
       "      <td>中专</td>\n",
       "      <td>0</td>\n",
       "      <td>【公安学】</td>\n",
       "      <td>深圳市</td>\n",
       "      <td>33.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>女</td>\n",
       "      <td>中专</td>\n",
       "      <td>0</td>\n",
       "      <td>【其他专业】</td>\n",
       "      <td>深圳市</td>\n",
       "      <td>31.857143</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>女</td>\n",
       "      <td>中专</td>\n",
       "      <td>0</td>\n",
       "      <td>【其他专业】</td>\n",
       "      <td>龙岗区</td>\n",
       "      <td>25.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>女</td>\n",
       "      <td>中专</td>\n",
       "      <td>0</td>\n",
       "      <td>【基础医学】</td>\n",
       "      <td>深圳市</td>\n",
       "      <td>31.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16905</th>\n",
       "      <td>男</td>\n",
       "      <td>高中（职高、技校）</td>\n",
       "      <td>34</td>\n",
       "      <td>物流管理</td>\n",
       "      <td>深圳市</td>\n",
       "      <td>49.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16906</th>\n",
       "      <td>男</td>\n",
       "      <td>高中（职高、技校）</td>\n",
       "      <td>34</td>\n",
       "      <td>电气工程及其自动化</td>\n",
       "      <td>龙岗区</td>\n",
       "      <td>53.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16907</th>\n",
       "      <td>男</td>\n",
       "      <td>高中（职高、技校）</td>\n",
       "      <td>36</td>\n",
       "      <td>机械制造及其自动化</td>\n",
       "      <td>光明区</td>\n",
       "      <td>55.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16908</th>\n",
       "      <td>男</td>\n",
       "      <td>高中（职高、技校）</td>\n",
       "      <td>39</td>\n",
       "      <td>电子信息科学与技术</td>\n",
       "      <td>深圳市</td>\n",
       "      <td>39.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16909</th>\n",
       "      <td>男</td>\n",
       "      <td>高中（职高、技校）</td>\n",
       "      <td>40</td>\n",
       "      <td>【其他专业】</td>\n",
       "      <td>深圳市</td>\n",
       "      <td>55.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>16910 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      性别         学历  工龄         专业 目前位置         年龄\n",
       "0      女         中专   0      【中医学】  深圳市  30.000000\n",
       "1      女         中专   0      【公安学】  深圳市  33.000000\n",
       "2      女         中专   0     【其他专业】  深圳市  31.857143\n",
       "3      女         中专   0     【其他专业】  龙岗区  25.000000\n",
       "4      女         中专   0     【基础医学】  深圳市  31.500000\n",
       "...   ..        ...  ..        ...  ...        ...\n",
       "16905  男  高中（职高、技校）  34       物流管理  深圳市  49.000000\n",
       "16906  男  高中（职高、技校）  34  电气工程及其自动化  龙岗区  53.000000\n",
       "16907  男  高中（职高、技校）  36  机械制造及其自动化  光明区  55.000000\n",
       "16908  男  高中（职高、技校）  39  电子信息科学与技术  深圳市  39.000000\n",
       "16909  男  高中（职高、技校）  40     【其他专业】  深圳市  55.000000\n",
       "\n",
       "[16910 rows x 6 columns]"
      ]
     },
     "execution_count": 230,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "应聘者索引 = df.groupby(by = [\"性别\",\"学历\",\"工龄\",\"专业\",\"目前位置\"])\\\n",
    "                         .agg({\"年龄\":\"mean\"})\\\n",
    "                         .reset_index()\n",
    "应聘者索引"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 231,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>性别</th>\n",
       "      <th>学历</th>\n",
       "      <th>工龄</th>\n",
       "      <th>专业</th>\n",
       "      <th>目前位置</th>\n",
       "      <th>年龄</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>6440</th>\n",
       "      <td>女</td>\n",
       "      <td>高中（职高、技校）</td>\n",
       "      <td>0</td>\n",
       "      <td>【中国语言文学】</td>\n",
       "      <td>深圳市</td>\n",
       "      <td>29.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6441</th>\n",
       "      <td>女</td>\n",
       "      <td>高中（职高、技校）</td>\n",
       "      <td>0</td>\n",
       "      <td>【交通运输工程】</td>\n",
       "      <td>深圳市</td>\n",
       "      <td>38.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6442</th>\n",
       "      <td>女</td>\n",
       "      <td>高中（职高、技校）</td>\n",
       "      <td>0</td>\n",
       "      <td>【体育学】</td>\n",
       "      <td>深圳市</td>\n",
       "      <td>27.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6443</th>\n",
       "      <td>女</td>\n",
       "      <td>高中（职高、技校）</td>\n",
       "      <td>0</td>\n",
       "      <td>【公共管理】</td>\n",
       "      <td>深圳市</td>\n",
       "      <td>29.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6444</th>\n",
       "      <td>女</td>\n",
       "      <td>高中（职高、技校）</td>\n",
       "      <td>0</td>\n",
       "      <td>【其他专业】</td>\n",
       "      <td>南山区</td>\n",
       "      <td>29.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16905</th>\n",
       "      <td>男</td>\n",
       "      <td>高中（职高、技校）</td>\n",
       "      <td>34</td>\n",
       "      <td>物流管理</td>\n",
       "      <td>深圳市</td>\n",
       "      <td>49.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16906</th>\n",
       "      <td>男</td>\n",
       "      <td>高中（职高、技校）</td>\n",
       "      <td>34</td>\n",
       "      <td>电气工程及其自动化</td>\n",
       "      <td>龙岗区</td>\n",
       "      <td>53.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16907</th>\n",
       "      <td>男</td>\n",
       "      <td>高中（职高、技校）</td>\n",
       "      <td>36</td>\n",
       "      <td>机械制造及其自动化</td>\n",
       "      <td>光明区</td>\n",
       "      <td>55.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16908</th>\n",
       "      <td>男</td>\n",
       "      <td>高中（职高、技校）</td>\n",
       "      <td>39</td>\n",
       "      <td>电子信息科学与技术</td>\n",
       "      <td>深圳市</td>\n",
       "      <td>39.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16909</th>\n",
       "      <td>男</td>\n",
       "      <td>高中（职高、技校）</td>\n",
       "      <td>40</td>\n",
       "      <td>【其他专业】</td>\n",
       "      <td>深圳市</td>\n",
       "      <td>55.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1820 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      性别         学历  工龄         专业 目前位置    年龄\n",
       "6440   女  高中（职高、技校）   0   【中国语言文学】  深圳市  29.0\n",
       "6441   女  高中（职高、技校）   0   【交通运输工程】  深圳市  38.0\n",
       "6442   女  高中（职高、技校）   0      【体育学】  深圳市  27.0\n",
       "6443   女  高中（职高、技校）   0     【公共管理】  深圳市  29.0\n",
       "6444   女  高中（职高、技校）   0     【其他专业】  南山区  29.0\n",
       "...   ..        ...  ..        ...  ...   ...\n",
       "16905  男  高中（职高、技校）  34       物流管理  深圳市  49.0\n",
       "16906  男  高中（职高、技校）  34  电气工程及其自动化  龙岗区  53.0\n",
       "16907  男  高中（职高、技校）  36  机械制造及其自动化  光明区  55.0\n",
       "16908  男  高中（职高、技校）  39  电子信息科学与技术  深圳市  39.0\n",
       "16909  男  高中（职高、技校）  40     【其他专业】  深圳市  55.0\n",
       "\n",
       "[1820 rows x 6 columns]"
      ]
     },
     "execution_count": 231,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "高中 = 应聘者工龄索引[应聘者工龄索引['学历'].isin(['高中（职高、技校）'])]#isin()筛选某列等于多个数值或者字符串\n",
    "高中"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 232,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>学历</th>\n",
       "      <th>工龄</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>中专</td>\n",
       "      <td>9.161999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>其它</td>\n",
       "      <td>10.108491</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>博士后</td>\n",
       "      <td>12.857143</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>博士研究生</td>\n",
       "      <td>12.818182</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>大专</td>\n",
       "      <td>8.342441</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>大学本科</td>\n",
       "      <td>7.090444</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>硕士研究生</td>\n",
       "      <td>8.467442</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>高中（职高、技校）</td>\n",
       "      <td>8.951607</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          学历         工龄\n",
       "0         中专   9.161999\n",
       "1         其它  10.108491\n",
       "2        博士后  12.857143\n",
       "3      博士研究生  12.818182\n",
       "4         大专   8.342441\n",
       "5       大学本科   7.090444\n",
       "6      硕士研究生   8.467442\n",
       "7  高中（职高、技校）   8.951607"
      ]
     },
     "execution_count": 232,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "应聘者平均工龄 = df.groupby(by = [\"学历\"])\\\n",
    "                         .agg({\"工龄\":\"mean\"})\\\n",
    "                         .reset_index()\n",
    "应聘者平均工龄"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 233,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.rcParams['font.sans-serif']=['SimHei']\n",
    "x = 应聘者平均工龄[\"学历\"]       #x轴数据\n",
    "y=  应聘者平均工龄[\"工龄\"]           #y轴数据\n",
    "plt.plot(x,y,color='m',mfc='w')\n",
    "plt.title('学历与平均工龄关系折线图')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 241,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>性别</th>\n",
       "      <th>年龄</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>女</td>\n",
       "      <td>29.828611</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>男</td>\n",
       "      <td>33.292816</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  性别         年龄\n",
       "0  女  29.828611\n",
       "1  男  33.292816"
      ]
     },
     "execution_count": 241,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "高中应聘者平均年龄 = 高中.groupby(by = [\"性别\"])\\\n",
    "                         .agg({\"年龄\":\"mean\"})\\\n",
    "                         .reset_index()\n",
    "高中应聘者平均年龄#.数量.to_dict() #.index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 236,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>性别</th>\n",
       "      <th>学历</th>\n",
       "      <th>工龄</th>\n",
       "      <th>专业</th>\n",
       "      <th>目前位置</th>\n",
       "      <th>年龄</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>7153</th>\n",
       "      <td>男</td>\n",
       "      <td>中专</td>\n",
       "      <td>0</td>\n",
       "      <td>【临床医学】</td>\n",
       "      <td>南山区</td>\n",
       "      <td>37.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7154</th>\n",
       "      <td>男</td>\n",
       "      <td>中专</td>\n",
       "      <td>0</td>\n",
       "      <td>【临床医学】</td>\n",
       "      <td>深圳市</td>\n",
       "      <td>28.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7155</th>\n",
       "      <td>男</td>\n",
       "      <td>中专</td>\n",
       "      <td>0</td>\n",
       "      <td>【公安学】</td>\n",
       "      <td>深圳市</td>\n",
       "      <td>32.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7156</th>\n",
       "      <td>男</td>\n",
       "      <td>中专</td>\n",
       "      <td>0</td>\n",
       "      <td>【其他专业】</td>\n",
       "      <td>宝安区</td>\n",
       "      <td>38.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7157</th>\n",
       "      <td>男</td>\n",
       "      <td>中专</td>\n",
       "      <td>0</td>\n",
       "      <td>【其他专业】</td>\n",
       "      <td>深圳市</td>\n",
       "      <td>31.166667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16905</th>\n",
       "      <td>男</td>\n",
       "      <td>高中（职高、技校）</td>\n",
       "      <td>34</td>\n",
       "      <td>物流管理</td>\n",
       "      <td>深圳市</td>\n",
       "      <td>49.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16906</th>\n",
       "      <td>男</td>\n",
       "      <td>高中（职高、技校）</td>\n",
       "      <td>34</td>\n",
       "      <td>电气工程及其自动化</td>\n",
       "      <td>龙岗区</td>\n",
       "      <td>53.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16907</th>\n",
       "      <td>男</td>\n",
       "      <td>高中（职高、技校）</td>\n",
       "      <td>36</td>\n",
       "      <td>机械制造及其自动化</td>\n",
       "      <td>光明区</td>\n",
       "      <td>55.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16908</th>\n",
       "      <td>男</td>\n",
       "      <td>高中（职高、技校）</td>\n",
       "      <td>39</td>\n",
       "      <td>电子信息科学与技术</td>\n",
       "      <td>深圳市</td>\n",
       "      <td>39.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16909</th>\n",
       "      <td>男</td>\n",
       "      <td>高中（职高、技校）</td>\n",
       "      <td>40</td>\n",
       "      <td>【其他专业】</td>\n",
       "      <td>深圳市</td>\n",
       "      <td>55.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>9757 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      性别         学历  工龄         专业 目前位置         年龄\n",
       "7153   男         中专   0     【临床医学】  南山区  37.000000\n",
       "7154   男         中专   0     【临床医学】  深圳市  28.000000\n",
       "7155   男         中专   0      【公安学】  深圳市  32.000000\n",
       "7156   男         中专   0     【其他专业】  宝安区  38.000000\n",
       "7157   男         中专   0     【其他专业】  深圳市  31.166667\n",
       "...   ..        ...  ..        ...  ...        ...\n",
       "16905  男  高中（职高、技校）  34       物流管理  深圳市  49.000000\n",
       "16906  男  高中（职高、技校）  34  电气工程及其自动化  龙岗区  53.000000\n",
       "16907  男  高中（职高、技校）  36  机械制造及其自动化  光明区  55.000000\n",
       "16908  男  高中（职高、技校）  39  电子信息科学与技术  深圳市  39.000000\n",
       "16909  男  高中（职高、技校）  40     【其他专业】  深圳市  55.000000\n",
       "\n",
       "[9757 rows x 6 columns]"
      ]
     },
     "execution_count": 236,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "男应聘者 = 应聘者索引[应聘者索引['性别'].isin(['男'])]#isin()筛选某列等于多个数值或者字符串\n",
    "男应聘者"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 256,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>性别</th>\n",
       "      <th>学历</th>\n",
       "      <th>工龄</th>\n",
       "      <th>专业</th>\n",
       "      <th>目前位置</th>\n",
       "      <th>年龄</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>女</td>\n",
       "      <td>中专</td>\n",
       "      <td>0</td>\n",
       "      <td>【中医学】</td>\n",
       "      <td>深圳市</td>\n",
       "      <td>30.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>女</td>\n",
       "      <td>中专</td>\n",
       "      <td>0</td>\n",
       "      <td>【公安学】</td>\n",
       "      <td>深圳市</td>\n",
       "      <td>33.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>女</td>\n",
       "      <td>中专</td>\n",
       "      <td>0</td>\n",
       "      <td>【其他专业】</td>\n",
       "      <td>深圳市</td>\n",
       "      <td>31.857143</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>女</td>\n",
       "      <td>中专</td>\n",
       "      <td>0</td>\n",
       "      <td>【其他专业】</td>\n",
       "      <td>龙岗区</td>\n",
       "      <td>25.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>女</td>\n",
       "      <td>中专</td>\n",
       "      <td>0</td>\n",
       "      <td>【基础医学】</td>\n",
       "      <td>深圳市</td>\n",
       "      <td>31.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7148</th>\n",
       "      <td>女</td>\n",
       "      <td>高中（职高、技校）</td>\n",
       "      <td>25</td>\n",
       "      <td>会计学</td>\n",
       "      <td>龙岗区</td>\n",
       "      <td>43.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7149</th>\n",
       "      <td>女</td>\n",
       "      <td>高中（职高、技校）</td>\n",
       "      <td>26</td>\n",
       "      <td>电子材料与元器件</td>\n",
       "      <td>深圳市</td>\n",
       "      <td>44.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7150</th>\n",
       "      <td>女</td>\n",
       "      <td>高中（职高、技校）</td>\n",
       "      <td>28</td>\n",
       "      <td>物流管理</td>\n",
       "      <td>宝安区</td>\n",
       "      <td>46.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7151</th>\n",
       "      <td>女</td>\n",
       "      <td>高中（职高、技校）</td>\n",
       "      <td>29</td>\n",
       "      <td>计算机应用技术</td>\n",
       "      <td>深圳市</td>\n",
       "      <td>29.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7152</th>\n",
       "      <td>女</td>\n",
       "      <td>高中（职高、技校）</td>\n",
       "      <td>39</td>\n",
       "      <td>【土木工程】</td>\n",
       "      <td>南山区</td>\n",
       "      <td>55.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>7153 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     性别         学历  工龄        专业 目前位置         年龄\n",
       "0     女         中专   0     【中医学】  深圳市  30.000000\n",
       "1     女         中专   0     【公安学】  深圳市  33.000000\n",
       "2     女         中专   0    【其他专业】  深圳市  31.857143\n",
       "3     女         中专   0    【其他专业】  龙岗区  25.000000\n",
       "4     女         中专   0    【基础医学】  深圳市  31.500000\n",
       "...  ..        ...  ..       ...  ...        ...\n",
       "7148  女  高中（职高、技校）  25       会计学  龙岗区  43.000000\n",
       "7149  女  高中（职高、技校）  26  电子材料与元器件  深圳市  44.000000\n",
       "7150  女  高中（职高、技校）  28      物流管理  宝安区  46.000000\n",
       "7151  女  高中（职高、技校）  29   计算机应用技术  深圳市  29.000000\n",
       "7152  女  高中（职高、技校）  39    【土木工程】  南山区  55.000000\n",
       "\n",
       "[7153 rows x 6 columns]"
      ]
     },
     "execution_count": 256,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "女应聘者 = 应聘者索引[应聘者索引['性别'].isin(['女'])]#isin()筛选某列等于多个数值或者字符串\n",
    "女应聘者"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 267,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[31.000000    860\n",
       " 30.000000    799\n",
       " 32.000000    713\n",
       " 29.000000    669\n",
       " 33.000000    651\n",
       "             ... \n",
       " 32.529412      1\n",
       " 34.189189      1\n",
       " 33.625000      1\n",
       " 31.764706      1\n",
       " 31.928571      1\n",
       " Name: 年龄, Length: 551, dtype: int64]"
      ]
     },
     "execution_count": 267,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "男位置counts=[男应聘者['年龄'].value_counts()]\n",
    "男位置counts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#分析森林数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 271,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Sgnyea</th>\n",
       "      <th>Prvcd</th>\n",
       "      <th>Prvnm</th>\n",
       "      <th>Frstua</th>\n",
       "      <th>Frsta</th>\n",
       "      <th>Plta</th>\n",
       "      <th>Frstcv</th>\n",
       "      <th>Stpcm</th>\n",
       "      <th>Frstcm</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2018</td>\n",
       "      <td>142</td>\n",
       "      <td>中国</td>\n",
       "      <td>32591.12</td>\n",
       "      <td>22044.62</td>\n",
       "      <td>8003.10</td>\n",
       "      <td>22.96</td>\n",
       "      <td>1900713.20</td>\n",
       "      <td>1756022.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2018</td>\n",
       "      <td>110000</td>\n",
       "      <td>北京市</td>\n",
       "      <td>107.10</td>\n",
       "      <td>71.82</td>\n",
       "      <td>43.48</td>\n",
       "      <td>43.77</td>\n",
       "      <td>3000.81</td>\n",
       "      <td>2437.36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2018</td>\n",
       "      <td>120000</td>\n",
       "      <td>天津市</td>\n",
       "      <td>20.39</td>\n",
       "      <td>13.64</td>\n",
       "      <td>12.98</td>\n",
       "      <td>12.07</td>\n",
       "      <td>620.56</td>\n",
       "      <td>460.27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2018</td>\n",
       "      <td>130000</td>\n",
       "      <td>河北省</td>\n",
       "      <td>775.64</td>\n",
       "      <td>502.69</td>\n",
       "      <td>263.54</td>\n",
       "      <td>26.78</td>\n",
       "      <td>15920.34</td>\n",
       "      <td>13737.98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2018</td>\n",
       "      <td>140000</td>\n",
       "      <td>山西省</td>\n",
       "      <td>787.25</td>\n",
       "      <td>321.09</td>\n",
       "      <td>167.63</td>\n",
       "      <td>20.50</td>\n",
       "      <td>14778.65</td>\n",
       "      <td>12923.37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>185</th>\n",
       "      <td>2013</td>\n",
       "      <td>610000</td>\n",
       "      <td>陕西省</td>\n",
       "      <td>1228.47</td>\n",
       "      <td>853.24</td>\n",
       "      <td>236.97</td>\n",
       "      <td>41.42</td>\n",
       "      <td>42416.05</td>\n",
       "      <td>39592.52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>186</th>\n",
       "      <td>2013</td>\n",
       "      <td>620000</td>\n",
       "      <td>甘肃省</td>\n",
       "      <td>1042.65</td>\n",
       "      <td>507.45</td>\n",
       "      <td>102.97</td>\n",
       "      <td>11.28</td>\n",
       "      <td>24054.88</td>\n",
       "      <td>21453.97</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>187</th>\n",
       "      <td>2013</td>\n",
       "      <td>630000</td>\n",
       "      <td>青海省</td>\n",
       "      <td>808.04</td>\n",
       "      <td>406.39</td>\n",
       "      <td>7.44</td>\n",
       "      <td>5.63</td>\n",
       "      <td>4884.43</td>\n",
       "      <td>4331.21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>188</th>\n",
       "      <td>2013</td>\n",
       "      <td>640000</td>\n",
       "      <td>宁夏回族自治区</td>\n",
       "      <td>180.10</td>\n",
       "      <td>61.80</td>\n",
       "      <td>14.43</td>\n",
       "      <td>11.89</td>\n",
       "      <td>872.56</td>\n",
       "      <td>660.33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>189</th>\n",
       "      <td>2013</td>\n",
       "      <td>650000</td>\n",
       "      <td>新疆维吾尔自治区</td>\n",
       "      <td>1099.71</td>\n",
       "      <td>698.25</td>\n",
       "      <td>94.00</td>\n",
       "      <td>4.24</td>\n",
       "      <td>38679.57</td>\n",
       "      <td>33654.09</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>190 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Sgnyea   Prvcd     Prvnm    Frstua     Frsta     Plta  Frstcv       Stpcm      Frstcm\n",
       "0      2018     142        中国  32591.12  22044.62  8003.10   22.96  1900713.20  1756022.99\n",
       "1      2018  110000       北京市    107.10     71.82    43.48   43.77     3000.81     2437.36\n",
       "2      2018  120000       天津市     20.39     13.64    12.98   12.07      620.56      460.27\n",
       "3      2018  130000       河北省    775.64    502.69   263.54   26.78    15920.34    13737.98\n",
       "4      2018  140000       山西省    787.25    321.09   167.63   20.50    14778.65    12923.37\n",
       "..      ...     ...       ...       ...       ...      ...     ...         ...         ...\n",
       "185    2013  610000       陕西省   1228.47    853.24   236.97   41.42    42416.05    39592.52\n",
       "186    2013  620000       甘肃省   1042.65    507.45   102.97   11.28    24054.88    21453.97\n",
       "187    2013  630000       青海省    808.04    406.39     7.44    5.63     4884.43     4331.21\n",
       "188    2013  640000   宁夏回族自治区    180.10     61.80    14.43   11.89      872.56      660.33\n",
       "189    2013  650000  新疆维吾尔自治区   1099.71    698.25    94.00    4.24    38679.57    33654.09\n",
       "\n",
       "[190 rows x 9 columns]"
      ]
     },
     "execution_count": 271,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv (\"./datas/forests/森林资源.csv\")\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 272,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>年份</th>\n",
       "      <th>森林火灾次数(次)</th>\n",
       "      <th>一般火灾次数(次)</th>\n",
       "      <th>较大火灾次数(次)</th>\n",
       "      <th>重大火灾次数(次)</th>\n",
       "      <th>特别重大火灾次数(次)</th>\n",
       "      <th>火场总面积(公顷)</th>\n",
       "      <th>受害森林面积(公顷)</th>\n",
       "      <th>天然林受害面积(公顷)</th>\n",
       "      <th>人工林受害面积(公顷)</th>\n",
       "      <th>森林火灾伤亡人数(人)</th>\n",
       "      <th>森林火灾死亡人数(人)</th>\n",
       "      <th>森林火灾其他损失折款(万元)</th>\n",
       "      <th>造林总面积(千公顷)</th>\n",
       "      <th>当年人工造林面积(千公顷)</th>\n",
       "      <th>当年飞机播种面积(千公顷)</th>\n",
       "      <th>无林地和疏林地新封山育林(千公顷)</th>\n",
       "      <th>用材林当年造林面积(千公顷)</th>\n",
       "      <th>经济林当年造林面积(千公顷)</th>\n",
       "      <th>防护林当年造林面积(千公顷)</th>\n",
       "      <th>薪炭林当年造林面积(千公顷)</th>\n",
       "      <th>特种用林当年造林面积(千公顷)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2018</td>\n",
       "      <td>2478</td>\n",
       "      <td>1579</td>\n",
       "      <td>894</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>28595.20</td>\n",
       "      <td>16309.07</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>39</td>\n",
       "      <td>NaN</td>\n",
       "      <td>20444.73</td>\n",
       "      <td>7299.47</td>\n",
       "      <td>3677.95</td>\n",
       "      <td>135.43</td>\n",
       "      <td>1785.07</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2017</td>\n",
       "      <td>3223</td>\n",
       "      <td>2258</td>\n",
       "      <td>958</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>44428.40</td>\n",
       "      <td>24502.43</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>46</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4624.06</td>\n",
       "      <td>7680.71</td>\n",
       "      <td>4295.89</td>\n",
       "      <td>141.22</td>\n",
       "      <td>1657.17</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2016</td>\n",
       "      <td>2034</td>\n",
       "      <td>1340</td>\n",
       "      <td>693</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>18161.46</td>\n",
       "      <td>6223.75</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>36</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4135.68</td>\n",
       "      <td>7203.51</td>\n",
       "      <td>3823.66</td>\n",
       "      <td>162.32</td>\n",
       "      <td>1953.64</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2015</td>\n",
       "      <td>2936</td>\n",
       "      <td>1676</td>\n",
       "      <td>1254</td>\n",
       "      <td>6.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>33076.62</td>\n",
       "      <td>12940.03</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>26</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6371.45</td>\n",
       "      <td>7683.70</td>\n",
       "      <td>4362.59</td>\n",
       "      <td>128.39</td>\n",
       "      <td>2152.88</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2014</td>\n",
       "      <td>3703</td>\n",
       "      <td>2080</td>\n",
       "      <td>1620</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>55339.60</td>\n",
       "      <td>19110.38</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>112</td>\n",
       "      <td>NaN</td>\n",
       "      <td>42512.79</td>\n",
       "      <td>5549.61</td>\n",
       "      <td>4052.91</td>\n",
       "      <td>108.06</td>\n",
       "      <td>1388.65</td>\n",
       "      <td>1092.35</td>\n",
       "      <td>1139.19</td>\n",
       "      <td>3238.66</td>\n",
       "      <td>36.95</td>\n",
       "      <td>42.46</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2013</td>\n",
       "      <td>3929</td>\n",
       "      <td>2347</td>\n",
       "      <td>1582</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>42890.42</td>\n",
       "      <td>13724.38</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>55</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6061.63</td>\n",
       "      <td>6100.06</td>\n",
       "      <td>4209.69</td>\n",
       "      <td>154.40</td>\n",
       "      <td>1735.97</td>\n",
       "      <td>1057.56</td>\n",
       "      <td>1233.68</td>\n",
       "      <td>3748.41</td>\n",
       "      <td>24.90</td>\n",
       "      <td>35.52</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     年份  森林火灾次数(次)  一般火灾次数(次)  较大火灾次数(次)  ...  经济林当年造林面积(千公顷)  防护林当年造林面积(千公顷)  薪炭林当年造林面积(千公顷)  特种用林当年造林面积(千公顷)\n",
       "0  2018       2478       1579        894  ...             NaN             NaN             NaN              NaN\n",
       "1  2017       3223       2258        958  ...             NaN             NaN             NaN              NaN\n",
       "2  2016       2034       1340        693  ...             NaN             NaN             NaN              NaN\n",
       "3  2015       2936       1676       1254  ...             NaN             NaN             NaN              NaN\n",
       "4  2014       3703       2080       1620  ...         1139.19         3238.66           36.95            42.46\n",
       "5  2013       3929       2347       1582  ...         1233.68         3748.41           24.90            35.52\n",
       "\n",
       "[6 rows x 22 columns]"
      ]
     },
     "execution_count": 272,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1 = pd.read_csv (\"./datas/forests/数据.csv\")\n",
    "df1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 273,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Sgnyea</th>\n",
       "      <th>Prvcd</th>\n",
       "      <th>Prvnm</th>\n",
       "      <th>Frstua</th>\n",
       "      <th>Frsta</th>\n",
       "      <th>Plta</th>\n",
       "      <th>Frstcv</th>\n",
       "      <th>Stpcm</th>\n",
       "      <th>Frstcm</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2018</td>\n",
       "      <td>110000</td>\n",
       "      <td>北京市</td>\n",
       "      <td>107.10</td>\n",
       "      <td>71.82</td>\n",
       "      <td>43.48</td>\n",
       "      <td>43.77</td>\n",
       "      <td>3000.81</td>\n",
       "      <td>2437.36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2018</td>\n",
       "      <td>120000</td>\n",
       "      <td>天津市</td>\n",
       "      <td>20.39</td>\n",
       "      <td>13.64</td>\n",
       "      <td>12.98</td>\n",
       "      <td>12.07</td>\n",
       "      <td>620.56</td>\n",
       "      <td>460.27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2018</td>\n",
       "      <td>130000</td>\n",
       "      <td>河北省</td>\n",
       "      <td>775.64</td>\n",
       "      <td>502.69</td>\n",
       "      <td>263.54</td>\n",
       "      <td>26.78</td>\n",
       "      <td>15920.34</td>\n",
       "      <td>13737.98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2018</td>\n",
       "      <td>140000</td>\n",
       "      <td>山西省</td>\n",
       "      <td>787.25</td>\n",
       "      <td>321.09</td>\n",
       "      <td>167.63</td>\n",
       "      <td>20.50</td>\n",
       "      <td>14778.65</td>\n",
       "      <td>12923.37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2018</td>\n",
       "      <td>150000</td>\n",
       "      <td>内蒙古自治区</td>\n",
       "      <td>4499.17</td>\n",
       "      <td>2614.85</td>\n",
       "      <td>600.01</td>\n",
       "      <td>22.10</td>\n",
       "      <td>166271.98</td>\n",
       "      <td>152704.12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>185</th>\n",
       "      <td>2013</td>\n",
       "      <td>610000</td>\n",
       "      <td>陕西省</td>\n",
       "      <td>1228.47</td>\n",
       "      <td>853.24</td>\n",
       "      <td>236.97</td>\n",
       "      <td>41.42</td>\n",
       "      <td>42416.05</td>\n",
       "      <td>39592.52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>186</th>\n",
       "      <td>2013</td>\n",
       "      <td>620000</td>\n",
       "      <td>甘肃省</td>\n",
       "      <td>1042.65</td>\n",
       "      <td>507.45</td>\n",
       "      <td>102.97</td>\n",
       "      <td>11.28</td>\n",
       "      <td>24054.88</td>\n",
       "      <td>21453.97</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>187</th>\n",
       "      <td>2013</td>\n",
       "      <td>630000</td>\n",
       "      <td>青海省</td>\n",
       "      <td>808.04</td>\n",
       "      <td>406.39</td>\n",
       "      <td>7.44</td>\n",
       "      <td>5.63</td>\n",
       "      <td>4884.43</td>\n",
       "      <td>4331.21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>188</th>\n",
       "      <td>2013</td>\n",
       "      <td>640000</td>\n",
       "      <td>宁夏回族自治区</td>\n",
       "      <td>180.10</td>\n",
       "      <td>61.80</td>\n",
       "      <td>14.43</td>\n",
       "      <td>11.89</td>\n",
       "      <td>872.56</td>\n",
       "      <td>660.33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>189</th>\n",
       "      <td>2013</td>\n",
       "      <td>650000</td>\n",
       "      <td>新疆维吾尔自治区</td>\n",
       "      <td>1099.71</td>\n",
       "      <td>698.25</td>\n",
       "      <td>94.00</td>\n",
       "      <td>4.24</td>\n",
       "      <td>38679.57</td>\n",
       "      <td>33654.09</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>184 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Sgnyea   Prvcd     Prvnm   Frstua    Frsta    Plta  Frstcv      Stpcm     Frstcm\n",
       "1      2018  110000       北京市   107.10    71.82   43.48   43.77    3000.81    2437.36\n",
       "2      2018  120000       天津市    20.39    13.64   12.98   12.07     620.56     460.27\n",
       "3      2018  130000       河北省   775.64   502.69  263.54   26.78   15920.34   13737.98\n",
       "4      2018  140000       山西省   787.25   321.09  167.63   20.50   14778.65   12923.37\n",
       "5      2018  150000    内蒙古自治区  4499.17  2614.85  600.01   22.10  166271.98  152704.12\n",
       "..      ...     ...       ...      ...      ...     ...     ...        ...        ...\n",
       "185    2013  610000       陕西省  1228.47   853.24  236.97   41.42   42416.05   39592.52\n",
       "186    2013  620000       甘肃省  1042.65   507.45  102.97   11.28   24054.88   21453.97\n",
       "187    2013  630000       青海省   808.04   406.39    7.44    5.63    4884.43    4331.21\n",
       "188    2013  640000   宁夏回族自治区   180.10    61.80   14.43   11.89     872.56     660.33\n",
       "189    2013  650000  新疆维吾尔自治区  1099.71   698.25   94.00    4.24   38679.57   33654.09\n",
       "\n",
       "[184 rows x 9 columns]"
      ]
     },
     "execution_count": 273,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = df[df['Prvnm'].str.contains('中国')==False ]#选取不包含中国的行\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 274,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>年份</th>\n",
       "      <th>地区代码</th>\n",
       "      <th>地区</th>\n",
       "      <th>林业用地面积</th>\n",
       "      <th>森林面积</th>\n",
       "      <th>人工林面积</th>\n",
       "      <th>森林覆盖率(%)</th>\n",
       "      <th>活立木总蓄积量</th>\n",
       "      <th>森林蓄积量</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2018</td>\n",
       "      <td>110000</td>\n",
       "      <td>北京市</td>\n",
       "      <td>107.10</td>\n",
       "      <td>71.82</td>\n",
       "      <td>43.48</td>\n",
       "      <td>43.77</td>\n",
       "      <td>3000.81</td>\n",
       "      <td>2437.36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2018</td>\n",
       "      <td>120000</td>\n",
       "      <td>天津市</td>\n",
       "      <td>20.39</td>\n",
       "      <td>13.64</td>\n",
       "      <td>12.98</td>\n",
       "      <td>12.07</td>\n",
       "      <td>620.56</td>\n",
       "      <td>460.27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2018</td>\n",
       "      <td>130000</td>\n",
       "      <td>河北省</td>\n",
       "      <td>775.64</td>\n",
       "      <td>502.69</td>\n",
       "      <td>263.54</td>\n",
       "      <td>26.78</td>\n",
       "      <td>15920.34</td>\n",
       "      <td>13737.98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2018</td>\n",
       "      <td>140000</td>\n",
       "      <td>山西省</td>\n",
       "      <td>787.25</td>\n",
       "      <td>321.09</td>\n",
       "      <td>167.63</td>\n",
       "      <td>20.50</td>\n",
       "      <td>14778.65</td>\n",
       "      <td>12923.37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2018</td>\n",
       "      <td>150000</td>\n",
       "      <td>内蒙古自治区</td>\n",
       "      <td>4499.17</td>\n",
       "      <td>2614.85</td>\n",
       "      <td>600.01</td>\n",
       "      <td>22.10</td>\n",
       "      <td>166271.98</td>\n",
       "      <td>152704.12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>185</th>\n",
       "      <td>2013</td>\n",
       "      <td>610000</td>\n",
       "      <td>陕西省</td>\n",
       "      <td>1228.47</td>\n",
       "      <td>853.24</td>\n",
       "      <td>236.97</td>\n",
       "      <td>41.42</td>\n",
       "      <td>42416.05</td>\n",
       "      <td>39592.52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>186</th>\n",
       "      <td>2013</td>\n",
       "      <td>620000</td>\n",
       "      <td>甘肃省</td>\n",
       "      <td>1042.65</td>\n",
       "      <td>507.45</td>\n",
       "      <td>102.97</td>\n",
       "      <td>11.28</td>\n",
       "      <td>24054.88</td>\n",
       "      <td>21453.97</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>187</th>\n",
       "      <td>2013</td>\n",
       "      <td>630000</td>\n",
       "      <td>青海省</td>\n",
       "      <td>808.04</td>\n",
       "      <td>406.39</td>\n",
       "      <td>7.44</td>\n",
       "      <td>5.63</td>\n",
       "      <td>4884.43</td>\n",
       "      <td>4331.21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>188</th>\n",
       "      <td>2013</td>\n",
       "      <td>640000</td>\n",
       "      <td>宁夏回族自治区</td>\n",
       "      <td>180.10</td>\n",
       "      <td>61.80</td>\n",
       "      <td>14.43</td>\n",
       "      <td>11.89</td>\n",
       "      <td>872.56</td>\n",
       "      <td>660.33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>189</th>\n",
       "      <td>2013</td>\n",
       "      <td>650000</td>\n",
       "      <td>新疆维吾尔自治区</td>\n",
       "      <td>1099.71</td>\n",
       "      <td>698.25</td>\n",
       "      <td>94.00</td>\n",
       "      <td>4.24</td>\n",
       "      <td>38679.57</td>\n",
       "      <td>33654.09</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>184 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       年份    地区代码        地区   林业用地面积     森林面积   人工林面积  森林覆盖率(%)    活立木总蓄积量      森林蓄积量\n",
       "1    2018  110000       北京市   107.10    71.82   43.48     43.77    3000.81    2437.36\n",
       "2    2018  120000       天津市    20.39    13.64   12.98     12.07     620.56     460.27\n",
       "3    2018  130000       河北省   775.64   502.69  263.54     26.78   15920.34   13737.98\n",
       "4    2018  140000       山西省   787.25   321.09  167.63     20.50   14778.65   12923.37\n",
       "5    2018  150000    内蒙古自治区  4499.17  2614.85  600.01     22.10  166271.98  152704.12\n",
       "..    ...     ...       ...      ...      ...     ...       ...        ...        ...\n",
       "185  2013  610000       陕西省  1228.47   853.24  236.97     41.42   42416.05   39592.52\n",
       "186  2013  620000       甘肃省  1042.65   507.45  102.97     11.28   24054.88   21453.97\n",
       "187  2013  630000       青海省   808.04   406.39    7.44      5.63    4884.43    4331.21\n",
       "188  2013  640000   宁夏回族自治区   180.10    61.80   14.43     11.89     872.56     660.33\n",
       "189  2013  650000  新疆维吾尔自治区  1099.71   698.25   94.00      4.24   38679.57   33654.09\n",
       "\n",
       "[184 rows x 9 columns]"
      ]
     },
     "execution_count": 274,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = df.rename(columns={\"Sgnyea\":\"年份\",\"Prvcd\":\"地区代码\",\"Prvnm\":\"地区\",\"Frstua\":\"林业用地面积\",\"Frsta\":\"森林面积\" ,\"Plta\":\"人工林面积\",\"Frstcv\":\"森林覆盖率(%)\",\"Stpcm\":\"活立木总蓄积量\",\"Frstcm\":\"森林蓄积量\" })\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 275,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>地区</th>\n",
       "      <th>年份</th>\n",
       "      <th>森林覆盖率(%)</th>\n",
       "      <th>森林蓄积量</th>\n",
       "      <th>林业用地面积</th>\n",
       "      <th>森林面积</th>\n",
       "      <th>人工林面积</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>上海市</td>\n",
       "      <td>2013</td>\n",
       "      <td>10.74</td>\n",
       "      <td>186.35</td>\n",
       "      <td>7.73</td>\n",
       "      <td>6.81</td>\n",
       "      <td>6.81</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>上海市</td>\n",
       "      <td>2014</td>\n",
       "      <td>10.74</td>\n",
       "      <td>186.35</td>\n",
       "      <td>7.73</td>\n",
       "      <td>6.81</td>\n",
       "      <td>6.81</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>上海市</td>\n",
       "      <td>2015</td>\n",
       "      <td>10.74</td>\n",
       "      <td>186.35</td>\n",
       "      <td>7.73</td>\n",
       "      <td>6.81</td>\n",
       "      <td>6.81</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>上海市</td>\n",
       "      <td>2016</td>\n",
       "      <td>10.74</td>\n",
       "      <td>186.35</td>\n",
       "      <td>7.73</td>\n",
       "      <td>6.81</td>\n",
       "      <td>6.81</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>上海市</td>\n",
       "      <td>2017</td>\n",
       "      <td>10.74</td>\n",
       "      <td>186.35</td>\n",
       "      <td>7.73</td>\n",
       "      <td>6.81</td>\n",
       "      <td>6.81</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>179</th>\n",
       "      <td>黑龙江省</td>\n",
       "      <td>2014</td>\n",
       "      <td>43.16</td>\n",
       "      <td>164487.01</td>\n",
       "      <td>2207.40</td>\n",
       "      <td>1962.13</td>\n",
       "      <td>246.53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>180</th>\n",
       "      <td>黑龙江省</td>\n",
       "      <td>2015</td>\n",
       "      <td>43.16</td>\n",
       "      <td>164487.01</td>\n",
       "      <td>2207.40</td>\n",
       "      <td>1962.13</td>\n",
       "      <td>246.53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>181</th>\n",
       "      <td>黑龙江省</td>\n",
       "      <td>2016</td>\n",
       "      <td>43.16</td>\n",
       "      <td>164487.01</td>\n",
       "      <td>2207.40</td>\n",
       "      <td>1962.13</td>\n",
       "      <td>246.53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>182</th>\n",
       "      <td>黑龙江省</td>\n",
       "      <td>2017</td>\n",
       "      <td>43.16</td>\n",
       "      <td>164487.01</td>\n",
       "      <td>2207.40</td>\n",
       "      <td>1962.13</td>\n",
       "      <td>246.53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>183</th>\n",
       "      <td>黑龙江省</td>\n",
       "      <td>2018</td>\n",
       "      <td>43.78</td>\n",
       "      <td>184704.09</td>\n",
       "      <td>2453.77</td>\n",
       "      <td>1990.46</td>\n",
       "      <td>243.26</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>184 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       地区    年份  森林覆盖率(%)      森林蓄积量   林业用地面积     森林面积   人工林面积\n",
       "0     上海市  2013     10.74     186.35     7.73     6.81    6.81\n",
       "1     上海市  2014     10.74     186.35     7.73     6.81    6.81\n",
       "2     上海市  2015     10.74     186.35     7.73     6.81    6.81\n",
       "3     上海市  2016     10.74     186.35     7.73     6.81    6.81\n",
       "4     上海市  2017     10.74     186.35     7.73     6.81    6.81\n",
       "..    ...   ...       ...        ...      ...      ...     ...\n",
       "179  黑龙江省  2014     43.16  164487.01  2207.40  1962.13  246.53\n",
       "180  黑龙江省  2015     43.16  164487.01  2207.40  1962.13  246.53\n",
       "181  黑龙江省  2016     43.16  164487.01  2207.40  1962.13  246.53\n",
       "182  黑龙江省  2017     43.16  164487.01  2207.40  1962.13  246.53\n",
       "183  黑龙江省  2018     43.78  184704.09  2453.77  1990.46  243.26\n",
       "\n",
       "[184 rows x 7 columns]"
      ]
     },
     "execution_count": 275,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "森林_城市 = df.groupby(by = [\"地区\",\"年份\",\"森林覆盖率(%)\",\"森林蓄积量\",'林业用地面积'])\\\n",
    "                         .agg({\"森林面积\":\"sum\",\"人工林面积\":\"sum\"})\\\n",
    "                         .reset_index()\n",
    "森林_城市#.数量.to_dict() #.index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 276,
   "metadata": {},
   "outputs": [],
   "source": [
    "森林_城市['年份'] = 森林_城市['年份'].astype('str')#astype转换numpy数组的数据类型"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 287,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>地区</th>\n",
       "      <th>年份</th>\n",
       "      <th>森林覆盖率(%)</th>\n",
       "      <th>森林蓄积量</th>\n",
       "      <th>林业用地面积</th>\n",
       "      <th>森林面积</th>\n",
       "      <th>人工林面积</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>上海市</td>\n",
       "      <td>2017</td>\n",
       "      <td>10.74</td>\n",
       "      <td>186.35</td>\n",
       "      <td>7.73</td>\n",
       "      <td>6.81</td>\n",
       "      <td>6.81</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>上海市</td>\n",
       "      <td>2018</td>\n",
       "      <td>14.04</td>\n",
       "      <td>449.59</td>\n",
       "      <td>10.19</td>\n",
       "      <td>8.90</td>\n",
       "      <td>8.90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>云南省</td>\n",
       "      <td>2017</td>\n",
       "      <td>50.03</td>\n",
       "      <td>169309.19</td>\n",
       "      <td>2501.04</td>\n",
       "      <td>1914.19</td>\n",
       "      <td>414.11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>云南省</td>\n",
       "      <td>2018</td>\n",
       "      <td>55.04</td>\n",
       "      <td>197265.84</td>\n",
       "      <td>2599.44</td>\n",
       "      <td>2106.16</td>\n",
       "      <td>507.68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>内蒙古自治区</td>\n",
       "      <td>2017</td>\n",
       "      <td>21.03</td>\n",
       "      <td>134530.48</td>\n",
       "      <td>4398.89</td>\n",
       "      <td>2487.90</td>\n",
       "      <td>331.65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>171</th>\n",
       "      <td>陕西省</td>\n",
       "      <td>2018</td>\n",
       "      <td>43.06</td>\n",
       "      <td>47866.70</td>\n",
       "      <td>1236.79</td>\n",
       "      <td>886.84</td>\n",
       "      <td>310.53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>176</th>\n",
       "      <td>青海省</td>\n",
       "      <td>2017</td>\n",
       "      <td>5.63</td>\n",
       "      <td>4331.21</td>\n",
       "      <td>808.04</td>\n",
       "      <td>406.39</td>\n",
       "      <td>7.44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>177</th>\n",
       "      <td>青海省</td>\n",
       "      <td>2018</td>\n",
       "      <td>5.82</td>\n",
       "      <td>4864.15</td>\n",
       "      <td>819.16</td>\n",
       "      <td>419.75</td>\n",
       "      <td>19.10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>182</th>\n",
       "      <td>黑龙江省</td>\n",
       "      <td>2017</td>\n",
       "      <td>43.16</td>\n",
       "      <td>164487.01</td>\n",
       "      <td>2207.40</td>\n",
       "      <td>1962.13</td>\n",
       "      <td>246.53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>183</th>\n",
       "      <td>黑龙江省</td>\n",
       "      <td>2018</td>\n",
       "      <td>43.78</td>\n",
       "      <td>184704.09</td>\n",
       "      <td>2453.77</td>\n",
       "      <td>1990.46</td>\n",
       "      <td>243.26</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>62 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         地区    年份  森林覆盖率(%)      森林蓄积量   林业用地面积     森林面积   人工林面积\n",
       "4       上海市  2017     10.74     186.35     7.73     6.81    6.81\n",
       "5       上海市  2018     14.04     449.59    10.19     8.90    8.90\n",
       "9       云南省  2017     50.03  169309.19  2501.04  1914.19  414.11\n",
       "10      云南省  2018     55.04  197265.84  2599.44  2106.16  507.68\n",
       "15   内蒙古自治区  2017     21.03  134530.48  4398.89  2487.90  331.65\n",
       "..      ...   ...       ...        ...      ...      ...     ...\n",
       "171     陕西省  2018     43.06   47866.70  1236.79   886.84  310.53\n",
       "176     青海省  2017      5.63    4331.21   808.04   406.39    7.44\n",
       "177     青海省  2018      5.82    4864.15   819.16   419.75   19.10\n",
       "182    黑龙江省  2017     43.16  164487.01  2207.40  1962.13  246.53\n",
       "183    黑龙江省  2018     43.78  184704.09  2453.77  1990.46  243.26\n",
       "\n",
       "[62 rows x 7 columns]"
      ]
     },
     "execution_count": 287,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "增长_2018 = 森林_城市[森林_城市['年份'].isin(['2017','2018'])]#isin()筛选某列等于多个数值或者字符串\n",
    "增长_2018"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 288,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>年份</th>\n",
       "      <th>受害森林面积(公顷)</th>\n",
       "      <th>造林总面积(千公顷)</th>\n",
       "      <th>森林火灾次数(次)</th>\n",
       "      <th>当年人工造林面积(千公顷)</th>\n",
       "      <th>火场总面积(公顷)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2013</td>\n",
       "      <td>13724.38</td>\n",
       "      <td>6100.06</td>\n",
       "      <td>3929</td>\n",
       "      <td>4209.69</td>\n",
       "      <td>42890.42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2014</td>\n",
       "      <td>19110.38</td>\n",
       "      <td>5549.61</td>\n",
       "      <td>3703</td>\n",
       "      <td>4052.91</td>\n",
       "      <td>55339.60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2015</td>\n",
       "      <td>12940.03</td>\n",
       "      <td>7683.70</td>\n",
       "      <td>2936</td>\n",
       "      <td>4362.59</td>\n",
       "      <td>33076.62</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2016</td>\n",
       "      <td>6223.75</td>\n",
       "      <td>7203.51</td>\n",
       "      <td>2034</td>\n",
       "      <td>3823.66</td>\n",
       "      <td>18161.46</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2017</td>\n",
       "      <td>24502.43</td>\n",
       "      <td>7680.71</td>\n",
       "      <td>3223</td>\n",
       "      <td>4295.89</td>\n",
       "      <td>44428.40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2018</td>\n",
       "      <td>16309.07</td>\n",
       "      <td>7299.47</td>\n",
       "      <td>2478</td>\n",
       "      <td>3677.95</td>\n",
       "      <td>28595.20</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     年份  受害森林面积(公顷)  造林总面积(千公顷)  森林火灾次数(次)  当年人工造林面积(千公顷)  火场总面积(公顷)\n",
       "0  2013    13724.38     6100.06       3929        4209.69   42890.42\n",
       "1  2014    19110.38     5549.61       3703        4052.91   55339.60\n",
       "2  2015    12940.03     7683.70       2936        4362.59   33076.62\n",
       "3  2016     6223.75     7203.51       2034        3823.66   18161.46\n",
       "4  2017    24502.43     7680.71       3223        4295.89   44428.40\n",
       "5  2018    16309.07     7299.47       2478        3677.95   28595.20"
      ]
     },
     "execution_count": 288,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 准备第三个报表\n",
    "森林变化 = df1.groupby(by = [\"年份\",\"受害森林面积(公顷)\",\"造林总面积(千公顷)\",'森林火灾次数(次)',\"当年人工造林面积(千公顷)\"])\\\n",
    "                         .agg({\"火场总面积(公顷)\":\"mean\"})\\\n",
    "                         .reset_index()\n",
    "森林变化"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 289,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'中国': '中国',\n",
       " '北京市': '北京',\n",
       " '天津市': '天津',\n",
       " '河北省': '河北',\n",
       " '山西省': '山西',\n",
       " '内蒙古自治区': '内蒙古自治区',\n",
       " '辽宁省': '辽宁',\n",
       " '吉林省': '吉林',\n",
       " '黑龙江省': '黑龙江',\n",
       " '上海市': '上海',\n",
       " '江苏省': '江苏',\n",
       " '浙江省': '浙江',\n",
       " '安徽省': '安徽',\n",
       " '福建省': '福建',\n",
       " '江西省': '江西',\n",
       " '山东省': '山东',\n",
       " '河南省': '河南',\n",
       " '湖北省': '湖北',\n",
       " '湖南省': '湖南',\n",
       " '广东省': '广东',\n",
       " '广西壮族自治区': '广西壮族自治区',\n",
       " '海南省': '海南',\n",
       " '重庆市': '重庆',\n",
       " '四川省': '四川',\n",
       " '贵州省': '贵州',\n",
       " '云南省': '云南',\n",
       " '西藏自治区': '西藏自治区',\n",
       " '陕西省': '陕西',\n",
       " '甘肃省': '甘肃',\n",
       " '青海省': '青海',\n",
       " '宁夏回族自治区': '宁夏回族自治区',\n",
       " '新疆维吾尔自治区': '新疆维吾尔自治区'}"
      ]
     },
     "execution_count": 289,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "搭桥表 = pd.read_excel(\"./datas/forests/地区中继表.xlsx\", index_col=0)\n",
    "搭桥 = 搭桥表[[\"地区\"]].to_dict()[\"地区\"]\n",
    "搭桥"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 290,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>level_0</th>\n",
       "      <th>index</th>\n",
       "      <th>地区</th>\n",
       "      <th>年份</th>\n",
       "      <th>森林覆盖率(%)</th>\n",
       "      <th>森林蓄积量</th>\n",
       "      <th>林业用地面积</th>\n",
       "      <th>森林面积</th>\n",
       "      <th>人工林面积</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>上海</td>\n",
       "      <td>2013</td>\n",
       "      <td>10.74</td>\n",
       "      <td>186.35</td>\n",
       "      <td>7.73</td>\n",
       "      <td>6.81</td>\n",
       "      <td>6.81</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>上海</td>\n",
       "      <td>2014</td>\n",
       "      <td>10.74</td>\n",
       "      <td>186.35</td>\n",
       "      <td>7.73</td>\n",
       "      <td>6.81</td>\n",
       "      <td>6.81</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>上海</td>\n",
       "      <td>2015</td>\n",
       "      <td>10.74</td>\n",
       "      <td>186.35</td>\n",
       "      <td>7.73</td>\n",
       "      <td>6.81</td>\n",
       "      <td>6.81</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>上海</td>\n",
       "      <td>2016</td>\n",
       "      <td>10.74</td>\n",
       "      <td>186.35</td>\n",
       "      <td>7.73</td>\n",
       "      <td>6.81</td>\n",
       "      <td>6.81</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>上海</td>\n",
       "      <td>2017</td>\n",
       "      <td>10.74</td>\n",
       "      <td>186.35</td>\n",
       "      <td>7.73</td>\n",
       "      <td>6.81</td>\n",
       "      <td>6.81</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>179</th>\n",
       "      <td>179</td>\n",
       "      <td>179</td>\n",
       "      <td>黑龙江</td>\n",
       "      <td>2014</td>\n",
       "      <td>43.16</td>\n",
       "      <td>164487.01</td>\n",
       "      <td>2207.40</td>\n",
       "      <td>1962.13</td>\n",
       "      <td>246.53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>180</th>\n",
       "      <td>180</td>\n",
       "      <td>180</td>\n",
       "      <td>黑龙江</td>\n",
       "      <td>2015</td>\n",
       "      <td>43.16</td>\n",
       "      <td>164487.01</td>\n",
       "      <td>2207.40</td>\n",
       "      <td>1962.13</td>\n",
       "      <td>246.53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>181</th>\n",
       "      <td>181</td>\n",
       "      <td>181</td>\n",
       "      <td>黑龙江</td>\n",
       "      <td>2016</td>\n",
       "      <td>43.16</td>\n",
       "      <td>164487.01</td>\n",
       "      <td>2207.40</td>\n",
       "      <td>1962.13</td>\n",
       "      <td>246.53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>182</th>\n",
       "      <td>182</td>\n",
       "      <td>182</td>\n",
       "      <td>黑龙江</td>\n",
       "      <td>2017</td>\n",
       "      <td>43.16</td>\n",
       "      <td>164487.01</td>\n",
       "      <td>2207.40</td>\n",
       "      <td>1962.13</td>\n",
       "      <td>246.53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>183</th>\n",
       "      <td>183</td>\n",
       "      <td>183</td>\n",
       "      <td>黑龙江</td>\n",
       "      <td>2018</td>\n",
       "      <td>43.78</td>\n",
       "      <td>184704.09</td>\n",
       "      <td>2453.77</td>\n",
       "      <td>1990.46</td>\n",
       "      <td>243.26</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>184 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     level_0  index   地区    年份  森林覆盖率(%)      森林蓄积量   林业用地面积     森林面积   人工林面积\n",
       "0          0      0   上海  2013     10.74     186.35     7.73     6.81    6.81\n",
       "1          1      1   上海  2014     10.74     186.35     7.73     6.81    6.81\n",
       "2          2      2   上海  2015     10.74     186.35     7.73     6.81    6.81\n",
       "3          3      3   上海  2016     10.74     186.35     7.73     6.81    6.81\n",
       "4          4      4   上海  2017     10.74     186.35     7.73     6.81    6.81\n",
       "..       ...    ...  ...   ...       ...        ...      ...      ...     ...\n",
       "179      179    179  黑龙江  2014     43.16  164487.01  2207.40  1962.13  246.53\n",
       "180      180    180  黑龙江  2015     43.16  164487.01  2207.40  1962.13  246.53\n",
       "181      181    181  黑龙江  2016     43.16  164487.01  2207.40  1962.13  246.53\n",
       "182      182    182  黑龙江  2017     43.16  164487.01  2207.40  1962.13  246.53\n",
       "183      183    183  黑龙江  2018     43.78  184704.09  2453.77  1990.46  243.26\n",
       "\n",
       "[184 rows x 9 columns]"
      ]
     },
     "execution_count": 290,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "搭桥后 = 森林_城市.reset_index().copy()#reset_index还原索引\n",
    "搭桥后['地区'] = [搭桥[x] for x in 搭桥后['地区']]#列表推导式把搭桥前的地区与搭桥后的替换\n",
    "搭桥后.reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 291,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.plotly.v1+json": {
       "config": {
        "plotlyServerURL": "https://plot.ly"
       },
       "data": [
        {
         "hovertemplate": "<b>%{hovertext}</b><br><br>年份=2017<br>森林面积=%{marker.size}<br>森林覆盖率(%)=%{marker.color}<extra></extra>",
         "hovertext": [
          "6.81",
          "1914.19",
          "2487.9",
          "58.81",
          "763.87",
          "1703.74",
          "11.16",
          "61.8",
          "380.42",
          "254.6",
          "282.41",
          "906.13",
          "1342.7",
          "698.25",
          "162.1",
          "1001.81",
          "439.33",
          "359.07",
          "601.36",
          "187.77",
          "713.86",
          "1011.94",
          "507.45",
          "801.27",
          "1471.56",
          "653.35",
          "557.31",
          "316.44",
          "853.24",
          "406.39",
          "1962.13"
         ],
         "ids": [
          "上海市",
          "云南省",
          "内蒙古自治区",
          "北京市",
          "吉林省",
          "四川省",
          "天津市",
          "宁夏回族自治区",
          "安徽省",
          "山东省",
          "山西省",
          "广东省",
          "广西壮族自治区",
          "新疆维吾尔自治区",
          "江苏省",
          "江西省",
          "河北省",
          "河南省",
          "浙江省",
          "海南省",
          "湖北省",
          "湖南省",
          "甘肃省",
          "福建省",
          "西藏自治区",
          "贵州省",
          "辽宁省",
          "重庆市",
          "陕西省",
          "青海省",
          "黑龙江省"
         ],
         "legendgroup": "",
         "marker": {
          "color": [
           10.74,
           50.03,
           21.03,
           35.84,
           40.38,
           35.22,
           9.87,
           11.89,
           27.53,
           16.73,
           18.03,
           51.26,
           56.51,
           4.24,
           15.8,
           60.01,
           23.41,
           21.5,
           59.07,
           55.38,
           38.4,
           47.77,
           11.28,
           65.95,
           11.98,
           37.09,
           38.24,
           38.43,
           41.42,
           5.63,
           43.16
          ],
          "coloraxis": "coloraxis",
          "size": [
           6.81,
           1914.19,
           2487.9,
           58.81,
           763.87,
           1703.74,
           11.16,
           61.8,
           380.42,
           254.6,
           282.41,
           906.13,
           1342.7,
           698.25,
           162.1,
           1001.81,
           439.33,
           359.07,
           601.36,
           187.77,
           713.86,
           1011.94,
           507.45,
           801.27,
           1471.56,
           653.35,
           557.31,
           316.44,
           853.24,
           406.39,
           1962.13
          ],
          "sizemode": "area",
          "sizeref": 0.7263472222222221,
          "symbol": "circle"
         },
         "mode": "markers",
         "name": "",
         "orientation": "v",
         "showlegend": false,
         "type": "scatter",
         "x": [
          6.81,
          1914.19,
          2487.9,
          58.81,
          763.87,
          1703.74,
          11.16,
          61.8,
          380.42,
          254.6,
          282.41,
          906.13,
          1342.7,
          698.25,
          162.1,
          1001.81,
          439.33,
          359.07,
          601.36,
          187.77,
          713.86,
          1011.94,
          507.45,
          801.27,
          1471.56,
          653.35,
          557.31,
          316.44,
          853.24,
          406.39,
          1962.13
         ],
         "xaxis": "x",
         "y": [
          10.74,
          50.03,
          21.03,
          35.84,
          40.38,
          35.22,
          9.87,
          11.89,
          27.53,
          16.73,
          18.03,
          51.26,
          56.51,
          4.24,
          15.8,
          60.01,
          23.41,
          21.5,
          59.07,
          55.38,
          38.4,
          47.77,
          11.28,
          65.95,
          11.98,
          37.09,
          38.24,
          38.43,
          41.42,
          5.63,
          43.16
         ],
         "yaxis": "y"
        }
       ],
       "frames": [
        {
         "data": [
          {
           "hovertemplate": "<b>%{hovertext}</b><br><br>年份=2017<br>森林面积=%{marker.size}<br>森林覆盖率(%)=%{marker.color}<extra></extra>",
           "hovertext": [
            "6.81",
            "1914.19",
            "2487.9",
            "58.81",
            "763.87",
            "1703.74",
            "11.16",
            "61.8",
            "380.42",
            "254.6",
            "282.41",
            "906.13",
            "1342.7",
            "698.25",
            "162.1",
            "1001.81",
            "439.33",
            "359.07",
            "601.36",
            "187.77",
            "713.86",
            "1011.94",
            "507.45",
            "801.27",
            "1471.56",
            "653.35",
            "557.31",
            "316.44",
            "853.24",
            "406.39",
            "1962.13"
           ],
           "ids": [
            "上海市",
            "云南省",
            "内蒙古自治区",
            "北京市",
            "吉林省",
            "四川省",
            "天津市",
            "宁夏回族自治区",
            "安徽省",
            "山东省",
            "山西省",
            "广东省",
            "广西壮族自治区",
            "新疆维吾尔自治区",
            "江苏省",
            "江西省",
            "河北省",
            "河南省",
            "浙江省",
            "海南省",
            "湖北省",
            "湖南省",
            "甘肃省",
            "福建省",
            "西藏自治区",
            "贵州省",
            "辽宁省",
            "重庆市",
            "陕西省",
            "青海省",
            "黑龙江省"
           ],
           "legendgroup": "",
           "marker": {
            "color": [
             10.74,
             50.03,
             21.03,
             35.84,
             40.38,
             35.22,
             9.87,
             11.89,
             27.53,
             16.73,
             18.03,
             51.26,
             56.51,
             4.24,
             15.8,
             60.01,
             23.41,
             21.5,
             59.07,
             55.38,
             38.4,
             47.77,
             11.28,
             65.95,
             11.98,
             37.09,
             38.24,
             38.43,
             41.42,
             5.63,
             43.16
            ],
            "coloraxis": "coloraxis",
            "size": [
             6.81,
             1914.19,
             2487.9,
             58.81,
             763.87,
             1703.74,
             11.16,
             61.8,
             380.42,
             254.6,
             282.41,
             906.13,
             1342.7,
             698.25,
             162.1,
             1001.81,
             439.33,
             359.07,
             601.36,
             187.77,
             713.86,
             1011.94,
             507.45,
             801.27,
             1471.56,
             653.35,
             557.31,
             316.44,
             853.24,
             406.39,
             1962.13
            ],
            "sizemode": "area",
            "sizeref": 0.7263472222222221,
            "symbol": "circle"
           },
           "mode": "markers",
           "name": "",
           "orientation": "v",
           "showlegend": false,
           "type": "scatter",
           "x": [
            6.81,
            1914.19,
            2487.9,
            58.81,
            763.87,
            1703.74,
            11.16,
            61.8,
            380.42,
            254.6,
            282.41,
            906.13,
            1342.7,
            698.25,
            162.1,
            1001.81,
            439.33,
            359.07,
            601.36,
            187.77,
            713.86,
            1011.94,
            507.45,
            801.27,
            1471.56,
            653.35,
            557.31,
            316.44,
            853.24,
            406.39,
            1962.13
           ],
           "xaxis": "x",
           "y": [
            10.74,
            50.03,
            21.03,
            35.84,
            40.38,
            35.22,
            9.87,
            11.89,
            27.53,
            16.73,
            18.03,
            51.26,
            56.51,
            4.24,
            15.8,
            60.01,
            23.41,
            21.5,
            59.07,
            55.38,
            38.4,
            47.77,
            11.28,
            65.95,
            11.98,
            37.09,
            38.24,
            38.43,
            41.42,
            5.63,
            43.16
           ],
           "yaxis": "y"
          }
         ],
         "name": "2017"
        },
        {
         "data": [
          {
           "hovertemplate": "<b>%{hovertext}</b><br><br>年份=2018<br>森林面积=%{marker.size}<br>森林覆盖率(%)=%{marker.color}<extra></extra>",
           "hovertext": [
            "8.9",
            "2106.16",
            "2614.85",
            "71.82",
            "784.87",
            "1839.77",
            "13.64",
            "65.6",
            "395.85",
            "266.51",
            "321.09",
            "945.98",
            "1429.65",
            "802.23",
            "155.99",
            "1021.02",
            "502.69",
            "403.18",
            "604.99",
            "194.49",
            "736.27",
            "1052.58",
            "509.73",
            "811.58",
            "1490.99",
            "771.03",
            "571.83",
            "354.97",
            "886.84",
            "419.75",
            "1990.46"
           ],
           "ids": [
            "上海市",
            "云南省",
            "内蒙古自治区",
            "北京市",
            "吉林省",
            "四川省",
            "天津市",
            "宁夏回族自治区",
            "安徽省",
            "山东省",
            "山西省",
            "广东省",
            "广西壮族自治区",
            "新疆维吾尔自治区",
            "江苏省",
            "江西省",
            "河北省",
            "河南省",
            "浙江省",
            "海南省",
            "湖北省",
            "湖南省",
            "甘肃省",
            "福建省",
            "西藏自治区",
            "贵州省",
            "辽宁省",
            "重庆市",
            "陕西省",
            "青海省",
            "黑龙江省"
           ],
           "legendgroup": "",
           "marker": {
            "color": [
             14.04,
             55.04,
             22.1,
             43.77,
             41.49,
             38.03,
             12.07,
             12.63,
             28.65,
             17.51,
             20.5,
             53.52,
             60.17,
             4.87,
             15.2,
             61.16,
             26.78,
             24.14,
             59.43,
             57.36,
             39.61,
             49.69,
             11.33,
             66.8,
             12.14,
             43.77,
             39.24,
             43.11,
             43.06,
             5.82,
             43.78
            ],
            "coloraxis": "coloraxis",
            "size": [
             8.9,
             2106.16,
             2614.85,
             71.82,
             784.87,
             1839.77,
             13.64,
             65.6,
             395.85,
             266.51,
             321.09,
             945.98,
             1429.65,
             802.23,
             155.99,
             1021.02,
             502.69,
             403.18,
             604.99,
             194.49,
             736.27,
             1052.58,
             509.73,
             811.58,
             1490.99,
             771.03,
             571.83,
             354.97,
             886.84,
             419.75,
             1990.46
            ],
            "sizemode": "area",
            "sizeref": 0.7263472222222221,
            "symbol": "circle"
           },
           "mode": "markers",
           "name": "",
           "orientation": "v",
           "showlegend": false,
           "type": "scatter",
           "x": [
            8.9,
            2106.16,
            2614.85,
            71.82,
            784.87,
            1839.77,
            13.64,
            65.6,
            395.85,
            266.51,
            321.09,
            945.98,
            1429.65,
            802.23,
            155.99,
            1021.02,
            502.69,
            403.18,
            604.99,
            194.49,
            736.27,
            1052.58,
            509.73,
            811.58,
            1490.99,
            771.03,
            571.83,
            354.97,
            886.84,
            419.75,
            1990.46
           ],
           "xaxis": "x",
           "y": [
            14.04,
            55.04,
            22.1,
            43.77,
            41.49,
            38.03,
            12.07,
            12.63,
            28.65,
            17.51,
            20.5,
            53.52,
            60.17,
            4.87,
            15.2,
            61.16,
            26.78,
            24.14,
            59.43,
            57.36,
            39.61,
            49.69,
            11.33,
            66.8,
            12.14,
            43.77,
            39.24,
            43.11,
            43.06,
            5.82,
            43.78
           ],
           "yaxis": "y"
          }
         ],
         "name": "2018"
        }
       ],
       "layout": {
        "coloraxis": {
         "colorbar": {
          "title": {
           "text": "森林覆盖率(%)"
          }
         },
         "colorscale": [
          [
           0,
           "#0d0887"
          ],
          [
           0.1111111111111111,
           "#46039f"
          ],
          [
           0.2222222222222222,
           "#7201a8"
          ],
          [
           0.3333333333333333,
           "#9c179e"
          ],
          [
           0.4444444444444444,
           "#bd3786"
          ],
          [
           0.5555555555555556,
           "#d8576b"
          ],
          [
           0.6666666666666666,
           "#ed7953"
          ],
          [
           0.7777777777777778,
           "#fb9f3a"
          ],
          [
           0.8888888888888888,
           "#fdca26"
          ],
          [
           1,
           "#f0f921"
          ]
         ]
        },
        "legend": {
         "itemsizing": "constant",
         "tracegroupgap": 0
        },
        "margin": {
         "t": 60
        },
        "sliders": [
         {
          "active": 0,
          "currentvalue": {
           "prefix": "年份="
          },
          "len": 0.9,
          "pad": {
           "b": 10,
           "t": 60
          },
          "steps": [
           {
            "args": [
             [
              "2017"
             ],
             {
              "frame": {
               "duration": 0,
               "redraw": false
              },
              "fromcurrent": true,
              "mode": "immediate",
              "transition": {
               "duration": 0,
               "easing": "linear"
              }
             }
            ],
            "label": "2017",
            "method": "animate"
           },
           {
            "args": [
             [
              "2018"
             ],
             {
              "frame": {
               "duration": 0,
               "redraw": false
              },
              "fromcurrent": true,
              "mode": "immediate",
              "transition": {
               "duration": 0,
               "easing": "linear"
              }
             }
            ],
            "label": "2018",
            "method": "animate"
           }
          ],
          "x": 0.1,
          "xanchor": "left",
          "y": 0,
          "yanchor": "top"
         }
        ],
        "template": {
         "data": {
          "bar": [
           {
            "error_x": {
             "color": "#2a3f5f"
            },
            "error_y": {
             "color": "#2a3f5f"
            },
            "marker": {
             "line": {
              "color": "#E5ECF6",
              "width": 0.5
             },
             "pattern": {
              "fillmode": "overlay",
              "size": 10,
              "solidity": 0.2
             }
            },
            "type": "bar"
           }
          ],
          "barpolar": [
           {
            "marker": {
             "line": {
              "color": "#E5ECF6",
              "width": 0.5
             },
             "pattern": {
              "fillmode": "overlay",
              "size": 10,
              "solidity": 0.2
             }
            },
            "type": "barpolar"
           }
          ],
          "carpet": [
           {
            "aaxis": {
             "endlinecolor": "#2a3f5f",
             "gridcolor": "white",
             "linecolor": "white",
             "minorgridcolor": "white",
             "startlinecolor": "#2a3f5f"
            },
            "baxis": {
             "endlinecolor": "#2a3f5f",
             "gridcolor": "white",
             "linecolor": "white",
             "minorgridcolor": "white",
             "startlinecolor": "#2a3f5f"
            },
            "type": "carpet"
           }
          ],
          "choropleth": [
           {
            "colorbar": {
             "outlinewidth": 0,
             "ticks": ""
            },
            "type": "choropleth"
           }
          ],
          "contour": [
           {
            "colorbar": {
             "outlinewidth": 0,
             "ticks": ""
            },
            "colorscale": [
             [
              0,
              "#0d0887"
             ],
             [
              0.1111111111111111,
              "#46039f"
             ],
             [
              0.2222222222222222,
              "#7201a8"
             ],
             [
              0.3333333333333333,
              "#9c179e"
             ],
             [
              0.4444444444444444,
              "#bd3786"
             ],
             [
              0.5555555555555556,
              "#d8576b"
             ],
             [
              0.6666666666666666,
              "#ed7953"
             ],
             [
              0.7777777777777778,
              "#fb9f3a"
             ],
             [
              0.8888888888888888,
              "#fdca26"
             ],
             [
              1,
              "#f0f921"
             ]
            ],
            "type": "contour"
           }
          ],
          "contourcarpet": [
           {
            "colorbar": {
             "outlinewidth": 0,
             "ticks": ""
            },
            "type": "contourcarpet"
           }
          ],
          "heatmap": [
           {
            "colorbar": {
             "outlinewidth": 0,
             "ticks": ""
            },
            "colorscale": [
             [
              0,
              "#0d0887"
             ],
             [
              0.1111111111111111,
              "#46039f"
             ],
             [
              0.2222222222222222,
              "#7201a8"
             ],
             [
              0.3333333333333333,
              "#9c179e"
             ],
             [
              0.4444444444444444,
              "#bd3786"
             ],
             [
              0.5555555555555556,
              "#d8576b"
             ],
             [
              0.6666666666666666,
              "#ed7953"
             ],
             [
              0.7777777777777778,
              "#fb9f3a"
             ],
             [
              0.8888888888888888,
              "#fdca26"
             ],
             [
              1,
              "#f0f921"
             ]
            ],
            "type": "heatmap"
           }
          ],
          "heatmapgl": [
           {
            "colorbar": {
             "outlinewidth": 0,
             "ticks": ""
            },
            "colorscale": [
             [
              0,
              "#0d0887"
             ],
             [
              0.1111111111111111,
              "#46039f"
             ],
             [
              0.2222222222222222,
              "#7201a8"
             ],
             [
              0.3333333333333333,
              "#9c179e"
             ],
             [
              0.4444444444444444,
              "#bd3786"
             ],
             [
              0.5555555555555556,
              "#d8576b"
             ],
             [
              0.6666666666666666,
              "#ed7953"
             ],
             [
              0.7777777777777778,
              "#fb9f3a"
             ],
             [
              0.8888888888888888,
              "#fdca26"
             ],
             [
              1,
              "#f0f921"
             ]
            ],
            "type": "heatmapgl"
           }
          ],
          "histogram": [
           {
            "marker": {
             "pattern": {
              "fillmode": "overlay",
              "size": 10,
              "solidity": 0.2
             }
            },
            "type": "histogram"
           }
          ],
          "histogram2d": [
           {
            "colorbar": {
             "outlinewidth": 0,
             "ticks": ""
            },
            "colorscale": [
             [
              0,
              "#0d0887"
             ],
             [
              0.1111111111111111,
              "#46039f"
             ],
             [
              0.2222222222222222,
              "#7201a8"
             ],
             [
              0.3333333333333333,
              "#9c179e"
             ],
             [
              0.4444444444444444,
              "#bd3786"
             ],
             [
              0.5555555555555556,
              "#d8576b"
             ],
             [
              0.6666666666666666,
              "#ed7953"
             ],
             [
              0.7777777777777778,
              "#fb9f3a"
             ],
             [
              0.8888888888888888,
              "#fdca26"
             ],
             [
              1,
              "#f0f921"
             ]
            ],
            "type": "histogram2d"
           }
          ],
          "histogram2dcontour": [
           {
            "colorbar": {
             "outlinewidth": 0,
             "ticks": ""
            },
            "colorscale": [
             [
              0,
              "#0d0887"
             ],
             [
              0.1111111111111111,
              "#46039f"
             ],
             [
              0.2222222222222222,
              "#7201a8"
             ],
             [
              0.3333333333333333,
              "#9c179e"
             ],
             [
              0.4444444444444444,
              "#bd3786"
             ],
             [
              0.5555555555555556,
              "#d8576b"
             ],
             [
              0.6666666666666666,
              "#ed7953"
             ],
             [
              0.7777777777777778,
              "#fb9f3a"
             ],
             [
              0.8888888888888888,
              "#fdca26"
             ],
             [
              1,
              "#f0f921"
             ]
            ],
            "type": "histogram2dcontour"
           }
          ],
          "mesh3d": [
           {
            "colorbar": {
             "outlinewidth": 0,
             "ticks": ""
            },
            "type": "mesh3d"
           }
          ],
          "parcoords": [
           {
            "line": {
             "colorbar": {
              "outlinewidth": 0,
              "ticks": ""
             }
            },
            "type": "parcoords"
           }
          ],
          "pie": [
           {
            "automargin": true,
            "type": "pie"
           }
          ],
          "scatter": [
           {
            "marker": {
             "colorbar": {
              "outlinewidth": 0,
              "ticks": ""
             }
            },
            "type": "scatter"
           }
          ],
          "scatter3d": [
           {
            "line": {
             "colorbar": {
              "outlinewidth": 0,
              "ticks": ""
             }
            },
            "marker": {
             "colorbar": {
              "outlinewidth": 0,
              "ticks": ""
             }
            },
            "type": "scatter3d"
           }
          ],
          "scattercarpet": [
           {
            "marker": {
             "colorbar": {
              "outlinewidth": 0,
              "ticks": ""
             }
            },
            "type": "scattercarpet"
           }
          ],
          "scattergeo": [
           {
            "marker": {
             "colorbar": {
              "outlinewidth": 0,
              "ticks": ""
             }
            },
            "type": "scattergeo"
           }
          ],
          "scattergl": [
           {
            "marker": {
             "colorbar": {
              "outlinewidth": 0,
              "ticks": ""
             }
            },
            "type": "scattergl"
           }
          ],
          "scattermapbox": [
           {
            "marker": {
             "colorbar": {
              "outlinewidth": 0,
              "ticks": ""
             }
            },
            "type": "scattermapbox"
           }
          ],
          "scatterpolar": [
           {
            "marker": {
             "colorbar": {
              "outlinewidth": 0,
              "ticks": ""
             }
            },
            "type": "scatterpolar"
           }
          ],
          "scatterpolargl": [
           {
            "marker": {
             "colorbar": {
              "outlinewidth": 0,
              "ticks": ""
             }
            },
            "type": "scatterpolargl"
           }
          ],
          "scatterternary": [
           {
            "marker": {
             "colorbar": {
              "outlinewidth": 0,
              "ticks": ""
             }
            },
            "type": "scatterternary"
           }
          ],
          "surface": [
           {
            "colorbar": {
             "outlinewidth": 0,
             "ticks": ""
            },
            "colorscale": [
             [
              0,
              "#0d0887"
             ],
             [
              0.1111111111111111,
              "#46039f"
             ],
             [
              0.2222222222222222,
              "#7201a8"
             ],
             [
              0.3333333333333333,
              "#9c179e"
             ],
             [
              0.4444444444444444,
              "#bd3786"
             ],
             [
              0.5555555555555556,
              "#d8576b"
             ],
             [
              0.6666666666666666,
              "#ed7953"
             ],
             [
              0.7777777777777778,
              "#fb9f3a"
             ],
             [
              0.8888888888888888,
              "#fdca26"
             ],
             [
              1,
              "#f0f921"
             ]
            ],
            "type": "surface"
           }
          ],
          "table": [
           {
            "cells": {
             "fill": {
              "color": "#EBF0F8"
             },
             "line": {
              "color": "white"
             }
            },
            "header": {
             "fill": {
              "color": "#C8D4E3"
             },
             "line": {
              "color": "white"
             }
            },
            "type": "table"
           }
          ]
         },
         "layout": {
          "annotationdefaults": {
           "arrowcolor": "#2a3f5f",
           "arrowhead": 0,
           "arrowwidth": 1
          },
          "autotypenumbers": "strict",
          "coloraxis": {
           "colorbar": {
            "outlinewidth": 0,
            "ticks": ""
           }
          },
          "colorscale": {
           "diverging": [
            [
             0,
             "#8e0152"
            ],
            [
             0.1,
             "#c51b7d"
            ],
            [
             0.2,
             "#de77ae"
            ],
            [
             0.3,
             "#f1b6da"
            ],
            [
             0.4,
             "#fde0ef"
            ],
            [
             0.5,
             "#f7f7f7"
            ],
            [
             0.6,
             "#e6f5d0"
            ],
            [
             0.7,
             "#b8e186"
            ],
            [
             0.8,
             "#7fbc41"
            ],
            [
             0.9,
             "#4d9221"
            ],
            [
             1,
             "#276419"
            ]
           ],
           "sequential": [
            [
             0,
             "#0d0887"
            ],
            [
             0.1111111111111111,
             "#46039f"
            ],
            [
             0.2222222222222222,
             "#7201a8"
            ],
            [
             0.3333333333333333,
             "#9c179e"
            ],
            [
             0.4444444444444444,
             "#bd3786"
            ],
            [
             0.5555555555555556,
             "#d8576b"
            ],
            [
             0.6666666666666666,
             "#ed7953"
            ],
            [
             0.7777777777777778,
             "#fb9f3a"
            ],
            [
             0.8888888888888888,
             "#fdca26"
            ],
            [
             1,
             "#f0f921"
            ]
           ],
           "sequentialminus": [
            [
             0,
             "#0d0887"
            ],
            [
             0.1111111111111111,
             "#46039f"
            ],
            [
             0.2222222222222222,
             "#7201a8"
            ],
            [
             0.3333333333333333,
             "#9c179e"
            ],
            [
             0.4444444444444444,
             "#bd3786"
            ],
            [
             0.5555555555555556,
             "#d8576b"
            ],
            [
             0.6666666666666666,
             "#ed7953"
            ],
            [
             0.7777777777777778,
             "#fb9f3a"
            ],
            [
             0.8888888888888888,
             "#fdca26"
            ],
            [
             1,
             "#f0f921"
            ]
           ]
          },
          "colorway": [
           "#636efa",
           "#EF553B",
           "#00cc96",
           "#ab63fa",
           "#FFA15A",
           "#19d3f3",
           "#FF6692",
           "#B6E880",
           "#FF97FF",
           "#FECB52"
          ],
          "font": {
           "color": "#2a3f5f"
          },
          "geo": {
           "bgcolor": "white",
           "lakecolor": "white",
           "landcolor": "#E5ECF6",
           "showlakes": true,
           "showland": true,
           "subunitcolor": "white"
          },
          "hoverlabel": {
           "align": "left"
          },
          "hovermode": "closest",
          "mapbox": {
           "style": "light"
          },
          "paper_bgcolor": "white",
          "plot_bgcolor": "#E5ECF6",
          "polar": {
           "angularaxis": {
            "gridcolor": "white",
            "linecolor": "white",
            "ticks": ""
           },
           "bgcolor": "#E5ECF6",
           "radialaxis": {
            "gridcolor": "white",
            "linecolor": "white",
            "ticks": ""
           }
          },
          "scene": {
           "xaxis": {
            "backgroundcolor": "#E5ECF6",
            "gridcolor": "white",
            "gridwidth": 2,
            "linecolor": "white",
            "showbackground": true,
            "ticks": "",
            "zerolinecolor": "white"
           },
           "yaxis": {
            "backgroundcolor": "#E5ECF6",
            "gridcolor": "white",
            "gridwidth": 2,
            "linecolor": "white",
            "showbackground": true,
            "ticks": "",
            "zerolinecolor": "white"
           },
           "zaxis": {
            "backgroundcolor": "#E5ECF6",
            "gridcolor": "white",
            "gridwidth": 2,
            "linecolor": "white",
            "showbackground": true,
            "ticks": "",
            "zerolinecolor": "white"
           }
          },
          "shapedefaults": {
           "line": {
            "color": "#2a3f5f"
           }
          },
          "ternary": {
           "aaxis": {
            "gridcolor": "white",
            "linecolor": "white",
            "ticks": ""
           },
           "baxis": {
            "gridcolor": "white",
            "linecolor": "white",
            "ticks": ""
           },
           "bgcolor": "#E5ECF6",
           "caxis": {
            "gridcolor": "white",
            "linecolor": "white",
            "ticks": ""
           }
          },
          "title": {
           "x": 0.05
          },
          "xaxis": {
           "automargin": true,
           "gridcolor": "white",
           "linecolor": "white",
           "ticks": "",
           "title": {
            "standoff": 15
           },
           "zerolinecolor": "white",
           "zerolinewidth": 2
          },
          "yaxis": {
           "automargin": true,
           "gridcolor": "white",
           "linecolor": "white",
           "ticks": "",
           "title": {
            "standoff": 15
           },
           "zerolinecolor": "white",
           "zerolinewidth": 2
          }
         }
        },
        "xaxis": {
         "anchor": "y",
         "domain": [
          0,
          1
         ],
         "range": [
          2,
          3.4771212547196617
         ],
         "title": {
          "text": "森林面积"
         },
         "type": "log"
        },
        "yaxis": {
         "anchor": "x",
         "domain": [
          0,
          1
         ],
         "range": [
          0,
          100
         ],
         "title": {
          "text": "森林覆盖率(%)"
         }
        }
       }
      },
      "text/html": [
       "<div>                            <div id=\"ed308582-cdaf-4a82-a7a5-17bbed79e178\" class=\"plotly-graph-div\" style=\"height:525px; width:100%;\"></div>            <script type=\"text/javascript\">                require([\"plotly\"], function(Plotly) {                    window.PLOTLYENV=window.PLOTLYENV || {};                                    if (document.getElementById(\"ed308582-cdaf-4a82-a7a5-17bbed79e178\")) {                    Plotly.newPlot(                        \"ed308582-cdaf-4a82-a7a5-17bbed79e178\",                        [{\"hovertemplate\":\"<b>%{hovertext}</b><br><br>\\u5e74\\u4efd=2017<br>\\u68ee\\u6797\\u9762\\u79ef=%{marker.size}<br>\\u68ee\\u6797\\u8986\\u76d6\\u7387(%)=%{marker.color}<extra></extra>\",\"hovertext\":[\"6.81\",\"1914.19\",\"2487.9\",\"58.81\",\"763.87\",\"1703.74\",\"11.16\",\"61.8\",\"380.42\",\"254.6\",\"282.41\",\"906.13\",\"1342.7\",\"698.25\",\"162.1\",\"1001.81\",\"439.33\",\"359.07\",\"601.36\",\"187.77\",\"713.86\",\"1011.94\",\"507.45\",\"801.27\",\"1471.56\",\"653.35\",\"557.31\",\"316.44\",\"853.24\",\"406.39\",\"1962.13\"],\"ids\":[\"\\u4e0a\\u6d77\\u5e02\",\"\\u4e91\\u5357\\u7701\",\"\\u5185\\u8499\\u53e4\\u81ea\\u6cbb\\u533a\",\"\\u5317\\u4eac\\u5e02\",\"\\u5409\\u6797\\u7701\",\"\\u56db\\u5ddd\\u7701\",\"\\u5929\\u6d25\\u5e02\",\"\\u5b81\\u590f\\u56de\\u65cf\\u81ea\\u6cbb\\u533a\",\"\\u5b89\\u5fbd\\u7701\",\"\\u5c71\\u4e1c\\u7701\",\"\\u5c71\\u897f\\u7701\",\"\\u5e7f\\u4e1c\\u7701\",\"\\u5e7f\\u897f\\u58ee\\u65cf\\u81ea\\u6cbb\\u533a\",\"\\u65b0\\u7586\\u7ef4\\u543e\\u5c14\\u81ea\\u6cbb\\u533a\",\"\\u6c5f\\u82cf\\u7701\",\"\\u6c5f\\u897f\\u7701\",\"\\u6cb3\\u5317\\u7701\",\"\\u6cb3\\u5357\\u7701\",\"\\u6d59\\u6c5f\\u7701\",\"\\u6d77\\u5357\\u7701\",\"\\u6e56\\u5317\\u7701\",\"\\u6e56\\u5357\\u7701\",\"\\u7518\\u8083\\u7701\",\"\\u798f\\u5efa\\u7701\",\"\\u897f\\u85cf\\u81ea\\u6cbb\\u533a\",\"\\u8d35\\u5dde\\u7701\",\"\\u8fbd\\u5b81\\u7701\",\"\\u91cd\\u5e86\\u5e02\",\"\\u9655\\u897f\\u7701\",\"\\u9752\\u6d77\\u7701\",\"\\u9ed1\\u9f99\\u6c5f\\u7701\"],\"legendgroup\":\"\",\"marker\":{\"color\":[10.74,50.03,21.03,35.84,40.38,35.22,9.87,11.89,27.53,16.73,18.03,51.26,56.51,4.24,15.8,60.01,23.41,21.5,59.07,55.38,38.4,47.77,11.28,65.95,11.98,37.09,38.24,38.43,41.42,5.63,43.16],\"coloraxis\":\"coloraxis\",\"size\":[6.81,1914.19,2487.9,58.81,763.87,1703.74,11.16,61.8,380.42,254.6,282.41,906.13,1342.7,698.25,162.1,1001.81,439.33,359.07,601.36,187.77,713.86,1011.94,507.45,801.27,1471.56,653.35,557.31,316.44,853.24,406.39,1962.13],\"sizemode\":\"area\",\"sizeref\":0.7263472222222221,\"symbol\":\"circle\"},\"mode\":\"markers\",\"name\":\"\",\"orientation\":\"v\",\"showlegend\":false,\"type\":\"scatter\",\"x\":[6.81,1914.19,2487.9,58.81,763.87,1703.74,11.16,61.8,380.42,254.6,282.41,906.13,1342.7,698.25,162.1,1001.81,439.33,359.07,601.36,187.77,713.86,1011.94,507.45,801.27,1471.56,653.35,557.31,316.44,853.24,406.39,1962.13],\"xaxis\":\"x\",\"y\":[10.74,50.03,21.03,35.84,40.38,35.22,9.87,11.89,27.53,16.73,18.03,51.26,56.51,4.24,15.8,60.01,23.41,21.5,59.07,55.38,38.4,47.77,11.28,65.95,11.98,37.09,38.24,38.43,41.42,5.63,43.16],\"yaxis\":\"y\"}],                        {\"coloraxis\":{\"colorbar\":{\"title\":{\"text\":\"\\u68ee\\u6797\\u8986\\u76d6\\u7387(%)\"}},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]]},\"legend\":{\"itemsizing\":\"constant\",\"tracegroupgap\":0},\"margin\":{\"t\":60},\"sliders\":[{\"active\":0,\"currentvalue\":{\"prefix\":\"\\u5e74\\u4efd=\"},\"len\":0.9,\"pad\":{\"b\":10,\"t\":60},\"steps\":[{\"args\":[[\"2017\"],{\"frame\":{\"duration\":0,\"redraw\":false},\"fromcurrent\":true,\"mode\":\"immediate\",\"transition\":{\"duration\":0,\"easing\":\"linear\"}}],\"label\":\"2017\",\"method\":\"animate\"},{\"args\":[[\"2018\"],{\"frame\":{\"duration\":0,\"redraw\":false},\"fromcurrent\":true,\"mode\":\"immediate\",\"transition\":{\"duration\":0,\"easing\":\"linear\"}}],\"label\":\"2018\",\"method\":\"animate\"}],\"x\":0.1,\"xanchor\":\"left\",\"y\":0,\"yanchor\":\"top\"}],\"template\":{\"data\":{\"bar\":[{\"error_x\":{\"color\":\"#2a3f5f\"},\"error_y\":{\"color\":\"#2a3f5f\"},\"marker\":{\"line\":{\"color\":\"#E5ECF6\",\"width\":0.5},\"pattern\":{\"fillmode\":\"overlay\",\"size\":10,\"solidity\":0.2}},\"type\":\"bar\"}],\"barpolar\":[{\"marker\":{\"line\":{\"color\":\"#E5ECF6\",\"width\":0.5},\"pattern\":{\"fillmode\":\"overlay\",\"size\":10,\"solidity\":0.2}},\"type\":\"barpolar\"}],\"carpet\":[{\"aaxis\":{\"endlinecolor\":\"#2a3f5f\",\"gridcolor\":\"white\",\"linecolor\":\"white\",\"minorgridcolor\":\"white\",\"startlinecolor\":\"#2a3f5f\"},\"baxis\":{\"endlinecolor\":\"#2a3f5f\",\"gridcolor\":\"white\",\"linecolor\":\"white\",\"minorgridcolor\":\"white\",\"startlinecolor\":\"#2a3f5f\"},\"type\":\"carpet\"}],\"choropleth\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"type\":\"choropleth\"}],\"contour\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"type\":\"contour\"}],\"contourcarpet\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"type\":\"contourcarpet\"}],\"heatmap\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"type\":\"heatmap\"}],\"heatmapgl\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"type\":\"heatmapgl\"}],\"histogram\":[{\"marker\":{\"pattern\":{\"fillmode\":\"overlay\",\"size\":10,\"solidity\":0.2}},\"type\":\"histogram\"}],\"histogram2d\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"type\":\"histogram2d\"}],\"histogram2dcontour\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"type\":\"histogram2dcontour\"}],\"mesh3d\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"type\":\"mesh3d\"}],\"parcoords\":[{\"line\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"parcoords\"}],\"pie\":[{\"automargin\":true,\"type\":\"pie\"}],\"scatter\":[{\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"scatter\"}],\"scatter3d\":[{\"line\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"scatter3d\"}],\"scattercarpet\":[{\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"scattercarpet\"}],\"scattergeo\":[{\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"scattergeo\"}],\"scattergl\":[{\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"scattergl\"}],\"scattermapbox\":[{\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"scattermapbox\"}],\"scatterpolar\":[{\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"scatterpolar\"}],\"scatterpolargl\":[{\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"scatterpolargl\"}],\"scatterternary\":[{\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"scatterternary\"}],\"surface\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"type\":\"surface\"}],\"table\":[{\"cells\":{\"fill\":{\"color\":\"#EBF0F8\"},\"line\":{\"color\":\"white\"}},\"header\":{\"fill\":{\"color\":\"#C8D4E3\"},\"line\":{\"color\":\"white\"}},\"type\":\"table\"}]},\"layout\":{\"annotationdefaults\":{\"arrowcolor\":\"#2a3f5f\",\"arrowhead\":0,\"arrowwidth\":1},\"autotypenumbers\":\"strict\",\"coloraxis\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"colorscale\":{\"diverging\":[[0,\"#8e0152\"],[0.1,\"#c51b7d\"],[0.2,\"#de77ae\"],[0.3,\"#f1b6da\"],[0.4,\"#fde0ef\"],[0.5,\"#f7f7f7\"],[0.6,\"#e6f5d0\"],[0.7,\"#b8e186\"],[0.8,\"#7fbc41\"],[0.9,\"#4d9221\"],[1,\"#276419\"]],\"sequential\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"sequentialminus\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]]},\"colorway\":[\"#636efa\",\"#EF553B\",\"#00cc96\",\"#ab63fa\",\"#FFA15A\",\"#19d3f3\",\"#FF6692\",\"#B6E880\",\"#FF97FF\",\"#FECB52\"],\"font\":{\"color\":\"#2a3f5f\"},\"geo\":{\"bgcolor\":\"white\",\"lakecolor\":\"white\",\"landcolor\":\"#E5ECF6\",\"showlakes\":true,\"showland\":true,\"subunitcolor\":\"white\"},\"hoverlabel\":{\"align\":\"left\"},\"hovermode\":\"closest\",\"mapbox\":{\"style\":\"light\"},\"paper_bgcolor\":\"white\",\"plot_bgcolor\":\"#E5ECF6\",\"polar\":{\"angularaxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\"},\"bgcolor\":\"#E5ECF6\",\"radialaxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\"}},\"scene\":{\"xaxis\":{\"backgroundcolor\":\"#E5ECF6\",\"gridcolor\":\"white\",\"gridwidth\":2,\"linecolor\":\"white\",\"showbackground\":true,\"ticks\":\"\",\"zerolinecolor\":\"white\"},\"yaxis\":{\"backgroundcolor\":\"#E5ECF6\",\"gridcolor\":\"white\",\"gridwidth\":2,\"linecolor\":\"white\",\"showbackground\":true,\"ticks\":\"\",\"zerolinecolor\":\"white\"},\"zaxis\":{\"backgroundcolor\":\"#E5ECF6\",\"gridcolor\":\"white\",\"gridwidth\":2,\"linecolor\":\"white\",\"showbackground\":true,\"ticks\":\"\",\"zerolinecolor\":\"white\"}},\"shapedefaults\":{\"line\":{\"color\":\"#2a3f5f\"}},\"ternary\":{\"aaxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\"},\"baxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\"},\"bgcolor\":\"#E5ECF6\",\"caxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\"}},\"title\":{\"x\":0.05},\"xaxis\":{\"automargin\":true,\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\",\"title\":{\"standoff\":15},\"zerolinecolor\":\"white\",\"zerolinewidth\":2},\"yaxis\":{\"automargin\":true,\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\",\"title\":{\"standoff\":15},\"zerolinecolor\":\"white\",\"zerolinewidth\":2}}},\"xaxis\":{\"anchor\":\"y\",\"domain\":[0.0,1.0],\"range\":[2.0,3.4771212547196617],\"title\":{\"text\":\"\\u68ee\\u6797\\u9762\\u79ef\"},\"type\":\"log\"},\"yaxis\":{\"anchor\":\"x\",\"domain\":[0.0,1.0],\"range\":[0,100],\"title\":{\"text\":\"\\u68ee\\u6797\\u8986\\u76d6\\u7387(%)\"}}},                        {\"responsive\": true}                    ).then(function(){\n",
       "                            Plotly.addFrames('ed308582-cdaf-4a82-a7a5-17bbed79e178', [{\"data\":[{\"hovertemplate\":\"<b>%{hovertext}</b><br><br>\\u5e74\\u4efd=2017<br>\\u68ee\\u6797\\u9762\\u79ef=%{marker.size}<br>\\u68ee\\u6797\\u8986\\u76d6\\u7387(%)=%{marker.color}<extra></extra>\",\"hovertext\":[\"6.81\",\"1914.19\",\"2487.9\",\"58.81\",\"763.87\",\"1703.74\",\"11.16\",\"61.8\",\"380.42\",\"254.6\",\"282.41\",\"906.13\",\"1342.7\",\"698.25\",\"162.1\",\"1001.81\",\"439.33\",\"359.07\",\"601.36\",\"187.77\",\"713.86\",\"1011.94\",\"507.45\",\"801.27\",\"1471.56\",\"653.35\",\"557.31\",\"316.44\",\"853.24\",\"406.39\",\"1962.13\"],\"ids\":[\"\\u4e0a\\u6d77\\u5e02\",\"\\u4e91\\u5357\\u7701\",\"\\u5185\\u8499\\u53e4\\u81ea\\u6cbb\\u533a\",\"\\u5317\\u4eac\\u5e02\",\"\\u5409\\u6797\\u7701\",\"\\u56db\\u5ddd\\u7701\",\"\\u5929\\u6d25\\u5e02\",\"\\u5b81\\u590f\\u56de\\u65cf\\u81ea\\u6cbb\\u533a\",\"\\u5b89\\u5fbd\\u7701\",\"\\u5c71\\u4e1c\\u7701\",\"\\u5c71\\u897f\\u7701\",\"\\u5e7f\\u4e1c\\u7701\",\"\\u5e7f\\u897f\\u58ee\\u65cf\\u81ea\\u6cbb\\u533a\",\"\\u65b0\\u7586\\u7ef4\\u543e\\u5c14\\u81ea\\u6cbb\\u533a\",\"\\u6c5f\\u82cf\\u7701\",\"\\u6c5f\\u897f\\u7701\",\"\\u6cb3\\u5317\\u7701\",\"\\u6cb3\\u5357\\u7701\",\"\\u6d59\\u6c5f\\u7701\",\"\\u6d77\\u5357\\u7701\",\"\\u6e56\\u5317\\u7701\",\"\\u6e56\\u5357\\u7701\",\"\\u7518\\u8083\\u7701\",\"\\u798f\\u5efa\\u7701\",\"\\u897f\\u85cf\\u81ea\\u6cbb\\u533a\",\"\\u8d35\\u5dde\\u7701\",\"\\u8fbd\\u5b81\\u7701\",\"\\u91cd\\u5e86\\u5e02\",\"\\u9655\\u897f\\u7701\",\"\\u9752\\u6d77\\u7701\",\"\\u9ed1\\u9f99\\u6c5f\\u7701\"],\"legendgroup\":\"\",\"marker\":{\"color\":[10.74,50.03,21.03,35.84,40.38,35.22,9.87,11.89,27.53,16.73,18.03,51.26,56.51,4.24,15.8,60.01,23.41,21.5,59.07,55.38,38.4,47.77,11.28,65.95,11.98,37.09,38.24,38.43,41.42,5.63,43.16],\"coloraxis\":\"coloraxis\",\"size\":[6.81,1914.19,2487.9,58.81,763.87,1703.74,11.16,61.8,380.42,254.6,282.41,906.13,1342.7,698.25,162.1,1001.81,439.33,359.07,601.36,187.77,713.86,1011.94,507.45,801.27,1471.56,653.35,557.31,316.44,853.24,406.39,1962.13],\"sizemode\":\"area\",\"sizeref\":0.7263472222222221,\"symbol\":\"circle\"},\"mode\":\"markers\",\"name\":\"\",\"orientation\":\"v\",\"showlegend\":false,\"type\":\"scatter\",\"x\":[6.81,1914.19,2487.9,58.81,763.87,1703.74,11.16,61.8,380.42,254.6,282.41,906.13,1342.7,698.25,162.1,1001.81,439.33,359.07,601.36,187.77,713.86,1011.94,507.45,801.27,1471.56,653.35,557.31,316.44,853.24,406.39,1962.13],\"xaxis\":\"x\",\"y\":[10.74,50.03,21.03,35.84,40.38,35.22,9.87,11.89,27.53,16.73,18.03,51.26,56.51,4.24,15.8,60.01,23.41,21.5,59.07,55.38,38.4,47.77,11.28,65.95,11.98,37.09,38.24,38.43,41.42,5.63,43.16],\"yaxis\":\"y\"}],\"name\":\"2017\"},{\"data\":[{\"hovertemplate\":\"<b>%{hovertext}</b><br><br>\\u5e74\\u4efd=2018<br>\\u68ee\\u6797\\u9762\\u79ef=%{marker.size}<br>\\u68ee\\u6797\\u8986\\u76d6\\u7387(%)=%{marker.color}<extra></extra>\",\"hovertext\":[\"8.9\",\"2106.16\",\"2614.85\",\"71.82\",\"784.87\",\"1839.77\",\"13.64\",\"65.6\",\"395.85\",\"266.51\",\"321.09\",\"945.98\",\"1429.65\",\"802.23\",\"155.99\",\"1021.02\",\"502.69\",\"403.18\",\"604.99\",\"194.49\",\"736.27\",\"1052.58\",\"509.73\",\"811.58\",\"1490.99\",\"771.03\",\"571.83\",\"354.97\",\"886.84\",\"419.75\",\"1990.46\"],\"ids\":[\"\\u4e0a\\u6d77\\u5e02\",\"\\u4e91\\u5357\\u7701\",\"\\u5185\\u8499\\u53e4\\u81ea\\u6cbb\\u533a\",\"\\u5317\\u4eac\\u5e02\",\"\\u5409\\u6797\\u7701\",\"\\u56db\\u5ddd\\u7701\",\"\\u5929\\u6d25\\u5e02\",\"\\u5b81\\u590f\\u56de\\u65cf\\u81ea\\u6cbb\\u533a\",\"\\u5b89\\u5fbd\\u7701\",\"\\u5c71\\u4e1c\\u7701\",\"\\u5c71\\u897f\\u7701\",\"\\u5e7f\\u4e1c\\u7701\",\"\\u5e7f\\u897f\\u58ee\\u65cf\\u81ea\\u6cbb\\u533a\",\"\\u65b0\\u7586\\u7ef4\\u543e\\u5c14\\u81ea\\u6cbb\\u533a\",\"\\u6c5f\\u82cf\\u7701\",\"\\u6c5f\\u897f\\u7701\",\"\\u6cb3\\u5317\\u7701\",\"\\u6cb3\\u5357\\u7701\",\"\\u6d59\\u6c5f\\u7701\",\"\\u6d77\\u5357\\u7701\",\"\\u6e56\\u5317\\u7701\",\"\\u6e56\\u5357\\u7701\",\"\\u7518\\u8083\\u7701\",\"\\u798f\\u5efa\\u7701\",\"\\u897f\\u85cf\\u81ea\\u6cbb\\u533a\",\"\\u8d35\\u5dde\\u7701\",\"\\u8fbd\\u5b81\\u7701\",\"\\u91cd\\u5e86\\u5e02\",\"\\u9655\\u897f\\u7701\",\"\\u9752\\u6d77\\u7701\",\"\\u9ed1\\u9f99\\u6c5f\\u7701\"],\"legendgroup\":\"\",\"marker\":{\"color\":[14.04,55.04,22.1,43.77,41.49,38.03,12.07,12.63,28.65,17.51,20.5,53.52,60.17,4.87,15.2,61.16,26.78,24.14,59.43,57.36,39.61,49.69,11.33,66.8,12.14,43.77,39.24,43.11,43.06,5.82,43.78],\"coloraxis\":\"coloraxis\",\"size\":[8.9,2106.16,2614.85,71.82,784.87,1839.77,13.64,65.6,395.85,266.51,321.09,945.98,1429.65,802.23,155.99,1021.02,502.69,403.18,604.99,194.49,736.27,1052.58,509.73,811.58,1490.99,771.03,571.83,354.97,886.84,419.75,1990.46],\"sizemode\":\"area\",\"sizeref\":0.7263472222222221,\"symbol\":\"circle\"},\"mode\":\"markers\",\"name\":\"\",\"orientation\":\"v\",\"showlegend\":false,\"type\":\"scatter\",\"x\":[8.9,2106.16,2614.85,71.82,784.87,1839.77,13.64,65.6,395.85,266.51,321.09,945.98,1429.65,802.23,155.99,1021.02,502.69,403.18,604.99,194.49,736.27,1052.58,509.73,811.58,1490.99,771.03,571.83,354.97,886.84,419.75,1990.46],\"xaxis\":\"x\",\"y\":[14.04,55.04,22.1,43.77,41.49,38.03,12.07,12.63,28.65,17.51,20.5,53.52,60.17,4.87,15.2,61.16,26.78,24.14,59.43,57.36,39.61,49.69,11.33,66.8,12.14,43.77,39.24,43.11,43.06,5.82,43.78],\"yaxis\":\"y\"}],\"name\":\"2018\"}]);\n",
       "                        }).then(function(){\n",
       "                            \n",
       "var gd = document.getElementById('ed308582-cdaf-4a82-a7a5-17bbed79e178');\n",
       "var x = new MutationObserver(function (mutations, observer) {{\n",
       "        var display = window.getComputedStyle(gd).display;\n",
       "        if (!display || display === 'none') {{\n",
       "            console.log([gd, 'removed!']);\n",
       "            Plotly.purge(gd);\n",
       "            observer.disconnect();\n",
       "        }}\n",
       "}});\n",
       "\n",
       "// Listen for the removal of the full notebook cells\n",
       "var notebookContainer = gd.closest('#notebook-container');\n",
       "if (notebookContainer) {{\n",
       "    x.observe(notebookContainer, {childList: true});\n",
       "}}\n",
       "\n",
       "// Listen for the clearing of the current output cell\n",
       "var outputEl = gd.closest('.output');\n",
       "if (outputEl) {{\n",
       "    x.observe(outputEl, {childList: true});\n",
       "}}\n",
       "\n",
       "                        })                };                });            </script>        </div>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import plotly.express as px\n",
    "增长_2018 = 增长_2018\n",
    "fig = px.scatter(增长_2018, x=\"森林面积\", y=\"森林覆盖率(%)\", animation_frame=\"年份\", animation_group=\"地区\",\n",
    "           size=\"森林面积\", color=\"森林覆盖率(%)\", hover_name=\"森林面积\",\n",
    "           log_x=True, size_max=60, range_x=[100,3000], range_y=[0,100])\n",
    "fig[\"layout\"].pop(\"updatemenus\") # optional, drop animation buttons\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.5"
  },
  "toc": {
   "base_numbering": 1,
   "nav_menu": {},
   "number_sections": true,
   "sideBar": true,
   "skip_h1_title": false,
   "title_cell": "Table of Contents",
   "title_sidebar": "Contents",
   "toc_cell": false,
   "toc_position": {},
   "toc_section_display": true,
   "toc_window_display": false
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
